This paper discusses the formation of an appropriate regression model in precipitation prediction.
Precipitation prediction has a major influence to multiply the agricultural production of potatoes in Tengger,
East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear
approach, the prediction of precipitation produces more accurate results. Genetic algorithm (GA)
functioning chooses precipitation period which forms the best model. To prevent early convergence,
testing the best combination value of crossover rate and mutation rate is done. To test the accuracy of the
predicted results are used Root Mean Square Error (RMSE) as a benchmark. Based on the RMSE value
of each method on every location, prediction using GA-Non-Linear Regression is better than Fuzzy
Tsukamoto for each location. Compared to Generalized Space-Time Autoregressive-Seemingly Unrelated
Regression (GSTAR-SUR), precipitation prediction using GA is better. This has been proved that for 3
locations GA is superior and on 1 location, GA has the least value of deviation level.
As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an optimal interpolation scheme for rainfall in Ningxia. The study area is located in northwest China. Meteorological stations distribute at a low density here. Six interpolation methods have been tested after exploring data. Cross-validation was used as the criterion to evaluate the accuracy of various methods. The best results were obtained by cokriging with elevation as the second variable, while the inverse distance weighting (IDW) preform worst. Three types of model in cokriging were compared, and Gaussian model is the best.
Improve Interval Optimization of FLR Using Auto-speed Acceleration AlgorithmTELKOMNIKA JOURNAL
Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results.
Defining Homogenous Climate zones of Bangladesh using Cluster AnalysisPremier Publishers
Climate zones of Bangladesh are identified by using mathematical methodology of cluster analysis. Monthly data from 34 climate stations for rainfall from 1991 to 2013 are used in the cluster analysis. Five Agglomerative Hierarchical clustering measures based on mostly used six proximity measures are chosen to perform the regionalization. Besides three popular measures: K-means, Fuzzy and density based clustering techniques are applied initially to decide the most suitable method for the identification of homogeneous region. Stability of the cluster is also tested based on nine validity indices. It is decided that Ward method based on Euclidean distance, K-means, Fuzzy are the most likely to yield acceptable results in this particular case, as is often the case in climatological research. In this analysis we found seven different climate zones in Bangladesh.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
2use of dss in ems to reduce responce timeosaleem0123
2use of dss in ems to reduce responce time
Reducing Ambulance Response Times Using Geospatial–Time Analysis of Ambulance Deployment
This study aimed to determine if a deployment strategy based on geospatial–time analysis is able to reduce ambulance response times for out‐of‐hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Linear Regressions of Predicting Rainfall over Kalay Regionijtsrd
Regression analysis is a statistical technique for investigating the relationship between variables. In this paper, rainfall and water level prediction models are discussed with the use of empirical statistical technique, Simple Linear Regression and analyzed the development of the predictive power of Linear Regression model to forecast the predicting rainfall and water level over Kalay in Sagaing Region for 10 years 2008 2017 .The data of the monthly rainfall and water level used in this study were obtained from Meteorology and Hydrology Department of Kalay, Myanmar. In July 2015, Kalay was affected by the floods. So the rainfall and water level are predicted for next five years in this paper. Ohnmar Myint "Linear Regressions of Predicting Rainfall over Kalay Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26711.pdf Paper URL: https://www.ijtsrd.com/mathemetics/computational-science/26711/linear-regressions-of-predicting-rainfall-over-kalay-region/ohnmar-myint
As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an optimal interpolation scheme for rainfall in Ningxia. The study area is located in northwest China. Meteorological stations distribute at a low density here. Six interpolation methods have been tested after exploring data. Cross-validation was used as the criterion to evaluate the accuracy of various methods. The best results were obtained by cokriging with elevation as the second variable, while the inverse distance weighting (IDW) preform worst. Three types of model in cokriging were compared, and Gaussian model is the best.
Improve Interval Optimization of FLR Using Auto-speed Acceleration AlgorithmTELKOMNIKA JOURNAL
Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results.
Defining Homogenous Climate zones of Bangladesh using Cluster AnalysisPremier Publishers
Climate zones of Bangladesh are identified by using mathematical methodology of cluster analysis. Monthly data from 34 climate stations for rainfall from 1991 to 2013 are used in the cluster analysis. Five Agglomerative Hierarchical clustering measures based on mostly used six proximity measures are chosen to perform the regionalization. Besides three popular measures: K-means, Fuzzy and density based clustering techniques are applied initially to decide the most suitable method for the identification of homogeneous region. Stability of the cluster is also tested based on nine validity indices. It is decided that Ward method based on Euclidean distance, K-means, Fuzzy are the most likely to yield acceptable results in this particular case, as is often the case in climatological research. In this analysis we found seven different climate zones in Bangladesh.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
2use of dss in ems to reduce responce timeosaleem0123
2use of dss in ems to reduce responce time
Reducing Ambulance Response Times Using Geospatial–Time Analysis of Ambulance Deployment
This study aimed to determine if a deployment strategy based on geospatial–time analysis is able to reduce ambulance response times for out‐of‐hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Linear Regressions of Predicting Rainfall over Kalay Regionijtsrd
Regression analysis is a statistical technique for investigating the relationship between variables. In this paper, rainfall and water level prediction models are discussed with the use of empirical statistical technique, Simple Linear Regression and analyzed the development of the predictive power of Linear Regression model to forecast the predicting rainfall and water level over Kalay in Sagaing Region for 10 years 2008 2017 .The data of the monthly rainfall and water level used in this study were obtained from Meteorology and Hydrology Department of Kalay, Myanmar. In July 2015, Kalay was affected by the floods. So the rainfall and water level are predicted for next five years in this paper. Ohnmar Myint "Linear Regressions of Predicting Rainfall over Kalay Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26711.pdf Paper URL: https://www.ijtsrd.com/mathemetics/computational-science/26711/linear-regressions-of-predicting-rainfall-over-kalay-region/ohnmar-myint
Gravitational search algorithm with chaotic map (gsa cm) for solving optimiza...eSAT Journals
Abstract
Gravitational Search Algorithm (GSA) is a newly heuristic algorithm inspired by nature which utilizes Newtonian gravity law and
mass interactions. It has captured much attention since it has provided higher performance in solving various optimization
problems. This study hybridizes the GSA and chaotic equations. Ten chaotic-based GSA (GSA-CM) methods, which define the
random selections by different chaotic maps, have been developed. The proposed methods have been applied to the minimization
of benchmark problems and the results have been compared. The obtained numeric results show that most of the proposed
algorithms have increased the performance of GSA and have developed its quality of solution.
Keywords: Computational Intelligence, Evolutionary Computation, Heuristic Algorithms, Chaotic Maps, Optimization
Methods.
Short-term load forecasting with using multiple linear regression IJECEIAES
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Hybrid model for forecasting space-time data with calendar variation effectsTELKOMNIKA JOURNAL
The aim of this research is to propose a new hybrid model, i.e. Generalized Space-Time
Autoregressive with Exogenous Variable and Neural Network (GSTARX-NN) model for forecasting
space-time data with calendar variation effect. GSTARX model represented as a linear component with
exogenous variable particularly an effect of calendar variation, such as Eid Fitr. Whereas, NN was a model
for handling a nonlinear component. There were two studies conducted in this research, i.e. simulation
studies and applications on monthly inflow and outflow currency data in Bank Indonesia at East Java
region. The simulation study showed that the hybrid GSTARX-NN model could capture well the data
patterns, i.e. trend, seasonal, calendar variation, and both linear and nonlinear noise series. Moreover,
based on RMSE at testing dataset, the results of application study on inflow and outflow data showed that
the hybrid GSTARX-NN models tend to give more accurate forecast than VARX and GSTARX models.
These results in line with the third M3 forecasting competition conclusion that stated hybrid or combining
models, in average, yielded better forecast than individual models.
Stochastic Model To Find The Effect Of Glucose-Dependent Insulinotropic Hormo...IJERA Editor
The study was to evaluate the GIP response when glucose was infused into the duodenum of eight healthy males
over 60 minutes on the first day into an isolated 60-cm segment of proximal small intestine. On the second day
the same amount of glucose was infused to entire small intestine. GIP did not differ between days. In this paper
the problem is investigated by largest discontinuity of Cauchy distribution.
"Fuzzy based approach for weather advisory system”iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ANNUAL PRECIPITATION IN SOUTHERN OF MADAGASCAR: MODELING USING HIGH ORDER FUZ...ijfls
The objective of this research is to find the best conventional high order fuzzy time series model for annual precipitation series in southern Madagascar. This work consists on finding the hyper parameters (number of partition of the universe of discourse and model order) to obtain the best conventional high
order fuzzy time series model for our experimental data. In previous works, entitled spatial and temporal variability of precipitation in southern Madagascar, we subdivided the study area between 22 ° S to 30 ° S latitude and 43 ° Eto 48 ° E longitude into four zones of homogeneous precipitation. In this article, we seek to model annual precipitation data representative of one of these four areas. These data were taken between 1979 and 2017. Our approach consists on subdividing the data: data obtained from 1979 to 2001 (60%) for the training and data from 2002 to 2017 (40%) to test the model. To determine the number of partitions and model order, we fix first the number of partitions to 10 and then to 15, 20, 25,30, 35, 40, 45 and 50.For each of these values, we vary the model order from 1 to 10.Thenwe locate the model order which corresponds to the minimum of the average curve between the Mean Absolute Errors (MAE) between the training data and the test data. Thus, the orders of the candidate model are 2, 3, 5, and 6.The next step is to fix the model order with the previous values and vary the number of partitions from 3 to 50.For each couple of hyper parameter of the model (number of partitions, model order), we locate the value of number of partitions corresponding to the minimum of the average curve between the absolute mean of the errors or MAE (Mean Absolute Error) between the train and test data. We obtain the hyper-parameter pairs (37, 2), (20, 3), (35, 5) and (35, 6).The first pair gives the lowest Mean Absolute Error. As a final result, we obtain the best high order fuzzy time series model with hyperparameters umber of partition equals thirty seven and of order equals two for annual precipitation in Southern of Madagascar.
Integration Method of Local-global SVR and Parallel Time Variant PSO in Water...TELKOMNIKA JOURNAL
Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather.
Time Series Data Analysis for Forecasting – A Literature ReviewIJMER
In today's world there is ample opportunity to clout the numerous sources of time series data
available for decision making. This time ordered data can be used to improve decision making if the data
is converted to information and then into knowledge which is called knowledge discovery. Data Mining
(DM) methods are being increasingly used in prediction with time series data, in addition to traditional
statistical approaches. This paper presents a literature review of the use of DM and statistical approaches
with time series data, focusing on weather prediction. This is an area that has been attracting a great deal
of attention from researchers in the field.
Alinteri Journal of Agriculture Sciences is being published online biannually as of 2007. The journal is an open access, international, double-blind peer-reviewed journal publishing research articles, Invited reviews, short communications, and letters to the Editor in the fields of agriculture, fisheries, veterinary, biology, and closely related disciplines. Alinteri Journal of Agriculture Sciences aims to create an environment for researchers to introduce, share, read, and discuss recent scientific progress. We adopt the policy of providing open access to readers who may be interested in recent developments.
Comparative Study of Machine Learning Algorithms for Rainfall Predictionijtsrd
Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901 2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status. Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D ""Comparative Study of Machine Learning Algorithms for Rainfall Prediction"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22961/comparative-study-of-machine-learning-algorithms-for-rainfall-prediction/mylapalle-yeshwanth
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
Gravitational search algorithm with chaotic map (gsa cm) for solving optimiza...eSAT Journals
Abstract
Gravitational Search Algorithm (GSA) is a newly heuristic algorithm inspired by nature which utilizes Newtonian gravity law and
mass interactions. It has captured much attention since it has provided higher performance in solving various optimization
problems. This study hybridizes the GSA and chaotic equations. Ten chaotic-based GSA (GSA-CM) methods, which define the
random selections by different chaotic maps, have been developed. The proposed methods have been applied to the minimization
of benchmark problems and the results have been compared. The obtained numeric results show that most of the proposed
algorithms have increased the performance of GSA and have developed its quality of solution.
Keywords: Computational Intelligence, Evolutionary Computation, Heuristic Algorithms, Chaotic Maps, Optimization
Methods.
Short-term load forecasting with using multiple linear regression IJECEIAES
In this paper short term load forecasting (STLF) is done with using multiple linear regression (MLR). A day ahead load forecasting is obtained in this paper. Regression coefficients were found out with the help of method of least square estimation. Load in electrical power system is dependent on temperature, due point and seasons and also load has correlation to the previous load consumption (Historical data). So the input variables are temperature, due point, load of prior day, hours, and load of prior week. To validate the model or check the accuracy of the model mean absolute percentage error is used and R squared is checked which is shown in result section. Using day ahead forecasted data weekly forecast is also obtained.
Hybrid model for forecasting space-time data with calendar variation effectsTELKOMNIKA JOURNAL
The aim of this research is to propose a new hybrid model, i.e. Generalized Space-Time
Autoregressive with Exogenous Variable and Neural Network (GSTARX-NN) model for forecasting
space-time data with calendar variation effect. GSTARX model represented as a linear component with
exogenous variable particularly an effect of calendar variation, such as Eid Fitr. Whereas, NN was a model
for handling a nonlinear component. There were two studies conducted in this research, i.e. simulation
studies and applications on monthly inflow and outflow currency data in Bank Indonesia at East Java
region. The simulation study showed that the hybrid GSTARX-NN model could capture well the data
patterns, i.e. trend, seasonal, calendar variation, and both linear and nonlinear noise series. Moreover,
based on RMSE at testing dataset, the results of application study on inflow and outflow data showed that
the hybrid GSTARX-NN models tend to give more accurate forecast than VARX and GSTARX models.
These results in line with the third M3 forecasting competition conclusion that stated hybrid or combining
models, in average, yielded better forecast than individual models.
Stochastic Model To Find The Effect Of Glucose-Dependent Insulinotropic Hormo...IJERA Editor
The study was to evaluate the GIP response when glucose was infused into the duodenum of eight healthy males
over 60 minutes on the first day into an isolated 60-cm segment of proximal small intestine. On the second day
the same amount of glucose was infused to entire small intestine. GIP did not differ between days. In this paper
the problem is investigated by largest discontinuity of Cauchy distribution.
"Fuzzy based approach for weather advisory system”iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ANNUAL PRECIPITATION IN SOUTHERN OF MADAGASCAR: MODELING USING HIGH ORDER FUZ...ijfls
The objective of this research is to find the best conventional high order fuzzy time series model for annual precipitation series in southern Madagascar. This work consists on finding the hyper parameters (number of partition of the universe of discourse and model order) to obtain the best conventional high
order fuzzy time series model for our experimental data. In previous works, entitled spatial and temporal variability of precipitation in southern Madagascar, we subdivided the study area between 22 ° S to 30 ° S latitude and 43 ° Eto 48 ° E longitude into four zones of homogeneous precipitation. In this article, we seek to model annual precipitation data representative of one of these four areas. These data were taken between 1979 and 2017. Our approach consists on subdividing the data: data obtained from 1979 to 2001 (60%) for the training and data from 2002 to 2017 (40%) to test the model. To determine the number of partitions and model order, we fix first the number of partitions to 10 and then to 15, 20, 25,30, 35, 40, 45 and 50.For each of these values, we vary the model order from 1 to 10.Thenwe locate the model order which corresponds to the minimum of the average curve between the Mean Absolute Errors (MAE) between the training data and the test data. Thus, the orders of the candidate model are 2, 3, 5, and 6.The next step is to fix the model order with the previous values and vary the number of partitions from 3 to 50.For each couple of hyper parameter of the model (number of partitions, model order), we locate the value of number of partitions corresponding to the minimum of the average curve between the absolute mean of the errors or MAE (Mean Absolute Error) between the train and test data. We obtain the hyper-parameter pairs (37, 2), (20, 3), (35, 5) and (35, 6).The first pair gives the lowest Mean Absolute Error. As a final result, we obtain the best high order fuzzy time series model with hyperparameters umber of partition equals thirty seven and of order equals two for annual precipitation in Southern of Madagascar.
Integration Method of Local-global SVR and Parallel Time Variant PSO in Water...TELKOMNIKA JOURNAL
Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather.
Time Series Data Analysis for Forecasting – A Literature ReviewIJMER
In today's world there is ample opportunity to clout the numerous sources of time series data
available for decision making. This time ordered data can be used to improve decision making if the data
is converted to information and then into knowledge which is called knowledge discovery. Data Mining
(DM) methods are being increasingly used in prediction with time series data, in addition to traditional
statistical approaches. This paper presents a literature review of the use of DM and statistical approaches
with time series data, focusing on weather prediction. This is an area that has been attracting a great deal
of attention from researchers in the field.
Alinteri Journal of Agriculture Sciences is being published online biannually as of 2007. The journal is an open access, international, double-blind peer-reviewed journal publishing research articles, Invited reviews, short communications, and letters to the Editor in the fields of agriculture, fisheries, veterinary, biology, and closely related disciplines. Alinteri Journal of Agriculture Sciences aims to create an environment for researchers to introduce, share, read, and discuss recent scientific progress. We adopt the policy of providing open access to readers who may be interested in recent developments.
Comparative Study of Machine Learning Algorithms for Rainfall Predictionijtsrd
Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901 2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status. Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D ""Comparative Study of Machine Learning Algorithms for Rainfall Prediction"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22961/comparative-study-of-machine-learning-algorithms-for-rainfall-prediction/mylapalle-yeshwanth
An Attempt To Use Interpolation to Predict Rainfall Intensities tor Crash Ana...IJMERJOURNAL
ABSTRACT: This study uses different interpolation techniques to predict rainfall intensity at locationsthat are not directly located near a rainfall gauges. The goal of being able to interpolate the rainfall intensity is to study its impact on traffic crashes. To perform the study, a collection of rainfall gauges in Alabama were used as subject locations where rainfall intensity was predicted from surrounding gauges, while also providing validation data to compare the predictions. Essentially, the actual rainfall intensities at existing gauges were interpolated using nearby gauges and the results were analyzed.The interpolation techniques used in the study included proximal, averaging and a distance weighted average. The results of the study indicated that none of the interpolation methodologies were sufficient to accurately predict the rainfall intensity values any significant distance from the actual gauges.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A comparative study of different imputation methods for daily rainfall data i...journalBEEI
Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.
This powerpoint presentation was done as part of the course STAT 591 titled Mater's Seminar during Third semester of MSc. Agricultural Statistics at Agricultural College, Bapatla under ANGRAU, Andhra Pradesh.
On the performance analysis of rainfall prediction using mutual information...IJECEIAES
Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10 longitude X 10 latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy f...IJECEIAES
The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other wellknown method in the literature.
A Novel Forecasting Based on Automatic-optimized Fuzzy Time SeriesTELKOMNIKA JOURNAL
In this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time
series to forecast Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor highorder
fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the
interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the
interval of main factor IIR and secondary factor SF, where SF = {Customer Price Index (CPI), the Bank of
Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (IDR/USD) Exchange rate, Money Supply}. The
proposed method gets lower root mean square error (RMSE) than previous methods.
Comparison of exponential smoothing and neural network method to forecast ric...TELKOMNIKA JOURNAL
Rice is the most important food commodity in Indonesia. In order to achieve affordability, and the fulfillment of the national food consumption according to the Indonesia law no. 18 of 2012, Indonesia needs information to support the government's policy regarding the collection, processing, analyzing, storing, presenting and disseminating. One manifestation of the Information availability to support the government’s policy is forecasting. Exponential smoothing and neural network methods are commonly used to forecasting because it provides a satisfactory result. Our study are comparing the variants of exponential and backpropagation model as a neural network to forecast rice production. The evaluation is summarized by utilizing Mean Square Percentage Error (MAPE), Mean Square Error (MSE). The results show that neural network method is preferable than the statistics method since it has lower MSE and MAPE values than statistics method.
Similar to Regression Modelling for Precipitation Prediction Using Genetic Algorithms (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
2. TELKOMNIKA ISSN: 1693-6930
Regression Modelling for Precipitation Prediction Using Genetic Algorithms (Asyrofa Rahmi)
1291
and genetic algorithms is used to select the best independent variables. Such as previous
methods, for some problems, GAs can be implemented using regression and has been shown
to provide more optimal results even using linear regression [15, 16]. Besides, according to
study that has been conducted by Majda and Harlim, one of the non-linear regression approach
that has proven the optimization than the other is a quadratic regression model [17].
In predicting precipitation optimal using quadratic regression, there is some precipitation
that period is not too have a strong influence in the movement pattern of precipitation.
Therefore, it needs to be selected in the period of the precipitation. However, to look for the
corresponding period, the problem is to have a lot of combinations of the period that may be an
optimal solution in predicting precipitation. If using the 20 periods the previous precipitation then
it is likely to look for a solution that is as many as 2
20
is 1048576 combinations. The number of
combinations of solutions that need to be searched also requires a lot of time. So, in order to
save time in the process of finding an optimal solution is to use a genetic algorithm.
This paper details an extension study of Rahmi, Mahmudy, and Setiawan [18]. The
previous study has applied GAs to find the most optimal number coefficients to form a
regression model that produces the minimal error. In that study, the coefficient of the regression
model is the entire period without considering which periods that most influential or not. This
study focuses on the formation of a regression model to choose how many periods are most
influential in predicting precipitation and also involves non-linear factors. Then the result of the
study provides better accuracy in predicting precipitation than the previous study [1, 10].
2. Regression Models
The prediction results in the regression model represented by the symbol Y as the
dependent variable that is influenced by several variables that indicate the data [X1, X2, … , Xk].
Regression equation used to examine the relationship dependent variable and several
independent variables [15, 16]. The composition of multiple regression models in general [16] is
shown in Equation (1).
(1)
Where k specifies the number of data in the past, b1, b2, …, bk are regression
coefficient, and is an independent variable in the form of random errors which has a mean of
zero and a variance in influencing the value of so that the Equation (1) can be transformed
into Equation (2).
(2)
Where t is time. So Equation (2) read that to find predictions on this period (t), then
require data on the previous period (t-1), data on 2 periods before (t-2) and data on k periods
before (t-k).
Equation (1) and (2) are multiple linear regression. For predicting, a non-linear
regression model can also be done. Determining the patterns of regression in predicting, it can
use another model of regression such as quadratic regression model. This model requires that
the elasticity of substitution becomes greater than zero and also diminishing marginal returns
must be valid so that appropriate to the effects of the problem. Quadratic regression is shown in
Equation (3) [17].
(3)
The quadratic regression is figured in Equation (3). Generally, quadratic regression has
an intercept, coefficients that apply using the single variable (X), and square variable (X
2
).
According to Equation (1), (2) and (3), non-linear prediction used is shown in Equation (4).
(4)
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Multiple non-linear regression models is shown in Equation (4). This equation consists
of intercept, multiple coefficients using variable and the square variable.
3. Methodology
Precipitation data used in this study is from 2005 through 2013 obtained since the start
of the dry season as training data. Each one data represents the period of 10 days which has a
value less than or equal to 50 mm. Then, the following data is representative of the next 10 days
which also has a value less than or equal to 50 mm.
There are 4 locations that have identified to be studied, Puspo, Sumber, Tosari and
Tutur. Overall precipitation data is divided into two types. Training data to determine the best
regression model based on the historical data and testing data to see the results of prediction.
The amount of data used is 320 for training and 100 for testing. Table 1 shows the precipitation
data of Puspo area.
Table 1. Precipitation Data of Puspo Area [1]
No YT Xt-1 Xt-2 … Xt-29 Xt-30 Xt-1
2
Xt-2
2
… Xt-29
2
Xt-30
2
1 4.5 14.5 5.364 0 0 14.52
5.3642
02
02
2 14.5 5.364 7.4 0 4.8 5.3642
7.42
02
4.82
3 5.364 7.4 25.8 4.8 0 7.42
25.82
4.82
02
…
320 18.7 19.5 12.5 0 0 19.52
12.52
02
02
Each row of Table 1 shows the original precipitation symbolized by Yt, then followed by
30 periods of precipitation symbolized by X, where Xt-1 is the precipitation of a period ago, then
Xt-2 is the precipitation of two periods before prior to the Xt-30 is precipitation 30 periods in
advance. Then because of the non-linear regression model used is quadratic then 30 of the next
field contains 30 squares of the same period the precipitation as before.
Precipitation prediction system implements regression model and genetic algorithm
(GAs). GAs applied to form new model and select the best equation of regression model. The
best equation regression resulted is shown which periods have increasingly influenced to
predict the precipitation on the next days. The regression model resulted is applied to compute
the prediction and error that used as a benchmark the best regression model.
GAs concept imitates natural and biological selection process [19-21]. Only the best
individuals indicated by fitness in order to survive in the next generation (iteration), then they
would be an optimal solution of a problem. The advantages of stochastic nature of Genetic
Algorithm are able to find the solution to the problems that are diverse and large-scale. Process
in the genetic algorithm starts with initialization phase, which creates individuals or
chromosomes that have a string of arrays which randomly generated. Each chromosome
represents a possible solution to a problem. By considering the fitness of a chromosome, the
greater fitness indicates that the chromosome is more qualified to be a solution [22]. The final
stage is the selection of choosing the new individuals as many as the population size obtained
by the individual parents and offsprings. The individuals results of the selection process are
alive in the next generation as the new population.
3.1. GAs Cycle
There are 2 kinds of cycles GAs. The selection process is used to select parent
(individual) in the first type of GAs [19, 22]. However, in this study using the other cycle one. It is
the parent chosen randomly and after the reproduction process is completed, the next stage is
to the selection process [20]. The overall GAs Cycle is shown as following:
Step 0 : Determining GAs parameters
Parameters on GAs are population size (popSize), crossover rate (Cr), mutation rate
(Mr) and maximum generation (maxGen)
Step 1 : Initialization of population
Let generation gen = 0, Generate the chromosome randomly as many as popSize
Step 2 : Reproduction
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Through crossover operator to produce offspring using popSize x Cr and mutation
operator to produce offspring using popSize x Mr
Step 3 : Selection
Select the popSize of chromosome from the collection of parents population and
offsprings
Step 4 : Let gen = gen + 1
When gen is equal to maxGen then stop, else do step 2.
3.2. Chromosome Representation
The candidates of optimal solution set at the time of chromosomes representation. The
visual representation of chromosomes that are used to solve problems in this study is shown in
Figure 1.
Figure 1. A Chromosome Representation
Chromosome representation shown in Figure 1 defines a chromosome that consists of
two segments with a length of 61 genes. The first gen is intended for choice using intercept or
not, the first 30 genes is single data for 30 periods before (t-30) at the location and the second
30 genes are intended for the squared data of 30 periods before (t-30).
The binary representation is a type of chromosome formation used in this study so the
content of each gene on the chromosome is 0 or 1. 0 represents the period data that has been
excluded and 1 represents the period data that has been included. The function of the binary
representation itself is to determine data on which periods that have been related to the
prediction. Then, the 1
st
gene of a chromosome is 1 which means the intercept is used. The 2
nd
gene is 1, which means the precipitation on a period before (Xt-1) is used and the 3
rd
gene worth
0 which means that the precipitation on two days earlier (Xt-2) is not be used, and so on until the
square precipitation value. The population is obtained from the set of chromosomes that have
represented a number of population size (popSize) which has been initialized in the beginning.
So, if the value of popSize is 10, then the population consists 10 chromosomes.
3.3. Fitness Function
The next process is to calculate the fitness of a chromosome (individual) that has been
presented. The chronology of fitness calculation is using the chromosome to change the
precipitation data based on the gene value. According to the new precipitation data that has
been changed, the data is processed using regression analysis then the coefficient regression
has been formed. From regression coefficient and the new precipitation data then the predicted
results can be generated. Forms regression model is considered good if obtaining prediction
results closer to the original results. One of the ways is using Root Mean Square Error (RMSE).
The smaller value of RMSE indicates a predicted value is closer to its original value.
Thus, in GAs process, the regression coefficients model formed from chromosome is said to be
optimal to be a solution if they have a great fitness value, while the proximity of good value is to
have a small RMSE, then the fitness value is inversely proportional to the value of RMSE.
Equation (5) is RMSE function and to get the fitness value using Equation (6)
√
∑
(5)
(6)
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Where n is the number of data used to predict. For training process, the amount of data
predicted as many as 320, so that the value of n is 320 so do the testing process, n is 100.
Examples fitness calculation process on one chromosome is shown in Figure 1 as the
formation of a model in Table 1. After the precipitation data in Table 1 was processed based on
the chromosome on Figure 1, Table 2 is formed as new precipitation data of Puspo Area.
Table 2. New Precipitation Data of Puspo Area Referring to Figure 1
No YT Xt-1 Xt-2 … Xt-29 Xt-30 Xt-1
2
Xt-2
2
… Xt-29
2
Xt-30
2
1 4.5 14.5 0 0 0 14.52
5.3642
0 02
2 14.5 5.364 0 0 0 5.3642
7.42
0 4.82
3 5.364 7.4 0 4.8 0 7.42
25.82
0 02
…
320 18.7 19.5 0 0 0 19.52
12.52
0 02
New data generated in Table 2 very different with Table 1. In Table 2, The Yt columns to
be equal to Yt column in Table 1 because it serves as an original value of precipitation. The next
column in Table 2 is Xt-1 also has the same value as the column Xt-1 in Table 1 because the
value of chromosomes in Figure 1 with a column Xt-1 is 1 which means that the variable Xt-1 is
used. Unlike the Xt-2 column in Table 2 are worth 0. This value differs from Xt-2 column in Table
1 which is 5.364. This change occurred because the column Xt-2 in Figure 1 has a value of 0
which means that the variable Xt-2 is not used.
The next process is calculating regression coefficients based on Table 2 through a
process of regression analysis. After the coefficient of each variable is obtained, then the
regression model are formed.
The regression coefficients model that have been obtained, is used to predict overall
precipitation based on the data in Table 2. Next, proceed by searching for RMSE values based
on Equation 5 to obtain fitness values using Equation 6.
3.4. Initialization Population
The population is the number of representation chromosome of the population size that
has been initialized at the beginning of the process in initialization parameters. A population
reflects the pool of possible solutions to a problem [21]. The example population and its fitness
of popSize 10 are shown in Table 3.
Table 3. An Initialization Population
P I Xt-1 Xt-2 … Xt-30 Xt-1
2
Xt-2
2
… Xt-29
2
Xt-30
2
Fitness
1 1 1 0 0 1 1 0 1 0.1703
2 1 0 0 1 1 0 1 1 0.1655
3 0 1 1 0 1 1 1 0 0.1673
…
10 0 1 0 0 0 0 1 1 0.1529
3.5. Reproduction
Reproduction is the operator of the formation of offspring (children / new individual) from
the individual parent population. The purpose of reproduction is to get the new individual who
has the diversity is not too far away from its parent so that it can obtain a more diverse solution
to resolve the problems [23]. Operator reproduction consists of two processes, namely
crossover and mutation [20].
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3.5.1. Crossover
The number of offspring result of the crossover derived from multiplying the value of Cr
and the size of the population (10) that has been initialized at the beginning (popSize). If Cr
value is 0.5 then the offspring resulted from crossover process is (10x0.5) 5. 5 offsprings (C1-C5)
produced from the crossover process included in the offspring pool for each generation.
In this study, the type of crossover used is the one-cut-point crossover. At this
crossover types, starting with determining the second parent (parent individuals) and the cutoff
point (gen). Then performed a cross between parents beginning of the first gene to gene
corresponding point of the intersection with the second parent of the gene point of the
intersection until the last gen. Examples crossover process determines the two parents (Parent
1 and Parent 2) and a randomly cut point on the 31
st
gen.
Figure 2. One-Cut Point Crossover Process
3.5.2. Mutation
Multiplying the value of Mr and size of the population that has been initialized at the
beginning is used to determine the number of offspring of the mutation process. If Mr value is
0.5 then the offspring resulted from mutation process is (10x0.5) 5. 5 offsprings (C6-C10)
produced from the mutation process also included in the offspring pool for each generation.
In this study, the mutation process chooses one parent and one gene randomly. Then
the value of the altered gene, if the value is 0 then it converted into 1 and vice versa.
Figure 3. Binary Mutation Process
3.6. Selection
The next stage is a selection process which aims to select the best individuals as many
as the population size of individuals parent in pool population (P) before and the new individuals
(offspring/C) of the reproduction process. Selection type used in this study is the selection of the
replacement. This selection model is quite effective in some cases optimization [21]. The virtue
of this kind of selection is allowing some individuals with low fitness value to be chosen as the
solution to the next generation or iteration because individuals with low fitness are also likely to
produce new and better individuals. Table 4 is the selection process using replacement. This
process starts from the fitness of overall results of reproduction process (offspring). C1 to C5 are
from crossover process and the other from mutation process.
On Table 4 there are 5 main columns. The first column is offspring symbolized by C and
the fitness of offspring on the second column. The third column is the original parent (OP) of
offspring. This column describes the origin parents of offspring. OP column has four sub-
columns. Original Parent 1 (OP1), Original Parent 2 (OP2), the fitness value of OP1 and Fitness
value of OP2. For offsprings from crossover process have two parents, OP1 and OP2 while the
mutation process have only one parent, OP1. Referring to offspring pool, offsprings from number
1 to 5 are from crossover process and the other are mutation process. The fourth main column
is a weak parent (WP). WP column has two sub-columns, WP itself and its fitness value. WP
column is a column contains a parent of original parents (OP), who has the weakest fitness.
If offsprings are from crossover process that has two original parents, the fitness of both original
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parents are compared. The original parent with the weakest fitness value is included in WP
column. The last main column is Result (R) Column. R column has three sub-columns. Chosen
Individual column and its fitness value also the description of it. Chosen individual is a column
containing the results of the comparison offspring itself with its parent regarding the fitness
value. The great value is the winner to be the chosen individual. If the offspring is the winner
then the description is “changed”. The meaning is the offspring replaced the weakest parent
position on population.
Table 4. Replacement Selection Process
C Fitness
Original Parent (OP) Weak Parent (WP) Result (R)
OP1 OP2 Fitness OP1 Fitness OP2 WP Fitness WP
Chosen
Individual
Fitness Description
1 0.1682 P1 P2 0.1703 0.1655 P2 0.1655 C1 0.1682 Changed
2 0.1463 P1 P20 0.1703 0.1529 P20 0.1529 P20 0.1529 Fixed
…
6 0.1627 P3 0.1673 P3 0.1673 P3 0.1673 Fixed
…
10 0.1722 P1(C1) 0.1682 P1 0.1682 C10 0.1722 Changed
For the selection process, there are 4 conditions that need to be considered. For first
offspring (C1), the fitness value is 0.1682. The original parents (OP) of C1 are two, there are OP1
and OP2. OP1 is P1, where P is Parent and OP2 is P2. Then find WP based on the fitness value
of OP1 and OP2. P2 is the weak parent because the fitness is 0.1655. After that, the fitness of
WP compared to the fitness of C1. The fitness of C1, 0.1682, is greater than WP, 0.1655. So C1
changed/replaced WP on population. The second condition is applied if the fitness of WP
greater than the offspring such as C2 case. Then the WP (P20) is fixed.
Next condition is for offspring of mutation process. The third condition is C6. Because
the original parent of mutation process is only one (P3), so that parent has been WP directly and
the fitness compared to the fitness of C6. Because fitness of P3 is greater than WP then P3 is
fixed. And the last condition is C10 case. OP of C10 is P1. Same as before, the parent has been
WP directly then compared to C10. Because P1 has been changed by C1, so C10 compared to
C1. The fitness of C10 is greater than C1 then C1 or P1 was changed/replaced to C10.
4. Results and Discussion
4.1. Population Size Testing
Tested population size is a multiple of 10. The initial value generation number is 100
with combined value of Cr and Mr are 0.5 and 0.5. Tests performed 10 times and recorded their
average fitness which is calculated to determine the optimum population size. The purpose of
the calculation of average fitness is to represent the optimal solution due to the stochastic
nature of the genetic algorithm so that always gives results that can vary each executed. Figure
4 is a graph of the results of population testing to choose the optimal population size.
Figure 4. Population Size Testing
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Based on the chart of population testing seen in Figure 4, the range between 10 to 100,
the starting point of optimal population size based on the value of fitness is 50. It can be seen in
the graph, that the fitness value on the size of the population under 50 has much difference is
0.0059, 0.0019, 0.0021, 0.00076 and 0.000999. Unlike when the points 50, 60, 70 and 80 which
have a difference in value that is not too far away. From point 50 to 60, the difference is
0.000467, while at the point of 60 to 70 is 0.0004669 and 0.000465 at point 70 to 80. Within the
range of 10 to 100, some of this difference indicates that the start point 50 have said nearly
optimal. The starting point from 80 to 100, the resulting fitness value is higher. It is also
reasonable. Because it is the first testing so the parameter values of generation, Cr and Mr
Combinations used have not been optimized. The next step is necessary to test those
parameters in order to get the solutions which achieve optimally.
4.2. Number of Generations Testing
The next parameter testing is a number of generations. The values tested are 10, 25 to
100 which multiples of 25, and 100 to 300 which multiples of 50. The initial value combinations
Mr and Cr values are 0.5 and 0.5. However, the population size used is the optimal size on the
test results of the previous population testing. Tests performed 10 times and recorded their best
fitness value and the average to determine the optimum number of generations. Figure 5 is a
trace graph of generation testing.
Figure 5. Generation Testing
According to the graph of generation testing seen in Figure 5, the best average fitness
is obtained on the number of generations 50. Since the average yield earned fitness achieve an
optimal point in generations to 50. The number of generations that were tested ceases to 300
because after 50 the average fitness obtained have the very little difference and not too far
away. The difference only almost 0.0003. If the test that has performed is greater than 300 then
the average fitness obtained is not too significant but it requires the longer computing time.
4.3. Testing Combination of Mr and Cr
Crossover rate (Cr) and Mutation rate (Mr) are the value that specifies the number of
new individuals produced. Cr as a determinant of the number of new individuals of the
crossover process and Mr as a determinant of the number of new individuals of the mutation
process. Hence, testing the combination of Mr and Cr equal to test the best combination the
number of new individual best of exploration and exploitation. The combination of Mr and Cr
tested is a value between 0 and 1. For this testing, the size of the population and the number of
generations used is the population size and the number of generations previous best in the test
population size of 50 and the number of generations 50. Tests performed 10 times and recorded
his best fitness value and the average is calculated to determine the optimal combination of Cr
and Mr value. Figure 6 is a trace graph testing of combination value of Mr and Cr where the
value of Mr starts at 0 and the value of Cr starts at 1.
Figure 6 tests graph of a combination of Mr and Cr. The average fitness of the best
combination of value Cr obtained at 0.7 and 0.3 for Mr value. Because the average fitness
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results obtained from the combination of the best values of other combinations. When combined
value of Cr less it produce an average fitness is not optimal because, in this phase, genetic
algorithms which work based on random search is not able to explore the search area
effectively.
Figure 6. Testing Combination of Mr and Cr
4.4. Analysis and Testing
Based on the testing parameters that have been done before, it is found that the optimal
population size, number of generation, also combination of Cr value and Mr value as shown in
Table 5.
Table 5. The Optimal Parameters GAs for Precipitation Prediction
Parameters Value
Population Size 50
Number of Generations 50
Cr Value 0.7
Mr Value 0.3
By using the optimal parameters obtained from testing, the optimal non-linear coefficient
is formed based on each location. Optimal coefficients produce a form of regression best to
solve the problems precipitation prediction. Based on the non-linear coefficients are formed, the
variables that influence and change at every location is different after trying five times. For
Puspo area, the influences variables are using intercept, 12 variables of X dan 11 variables of
X
2
. In Sumber area using intercept, 11 variables of X dan 11 for X
2
. Tosari area also using
intercept, 17 variables of X and variable of X
2
18. The last area is also kept using intercept,
variables X 14 dan 17 for X
2
. Table 6 is the result of precipitation prediction for four locations
using some methods.
Table 6. The Precipitation Prediction Result of 4 Locations and Related Methods
Locations Original Precipitation GSTAR-SUR [1]
Fuzzy
Tsukamoto [10]
GAs-Non-Linear Regression
Puspo 4.5 2.91 11.9898 2.47
Sumber 0 2.18 11.4797 1.904
Tosari 4.8 1.34 11.411 7.27
Tutur 12.6 1.996 12.3064 11.36
To determine the accuracy of precipitation prediction of each location, then it is tested
using 100 test data for each location. The acquisition of precipitation predicted results using
Genetic Algorithm – Non-Linear Regression System compared to the precipitation prediction
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using previous methods, they are GSTAR-SUR [1] and Fuzzy Tsukamoto [10]. The RMSE value
of each method has shown in Table 7.
Table 7. Comparing RMSE Value of GAs Non-Linear
Locations GSTAR-SUR [1]
Fuzzy
Tsukamoto [10]
GAs-Non-Linear
Regression
Deviation Level
(GSTAR-SUR)
Puspo 4.90 8.95 5.095 0.038273
Sumber 6.69 9.64 6.03 0.098655
Tosari 7.92 8.81 7.04 0.111111
Tutur 10.89 8.64 4.77 0.561983
Table 7 shows that the optimal precipitation predictions obtained by using Genetic
Algorithms are more accurate than Fuzzy Tsukamoto. Compared to GSTAR-SUR, prediction
using GAs also give the optimal results on Sumber, Tosari and Tutur area. Precipitation
prediction using GAs is not as well as GSTAR. However, the deviation level of GAs and GSTAR
on Puspo area shows the least value. This can prove that the result of precipitation prediction
using Genetic Algorithm – Non Linear Regression is better than the others. The deviation level
on Table 7 only between GAs Non-Linear Regression and GSTAR-SUR Model. The deviation
level between GAs Non-Linear Regression and Fuzzy Tsukamoto are not necessary because
the value of GAs Non-Linear Regression is overall superior to Fuzzy Tsukamoto. Based on the
result that has been declaring, GA-Non-Linear Regression very recommended for solving the
problem of precipitation prediction.
5. Conclusion
Predicting precipitation can be done effectively by generating non-linear regression
model using genetic algorithm. Non-linear approach undertaken in this study was using multiple
quadratic regression models. In the process of finding solutions precipitation prediction, binary
chromosome representation model can be implemented to look for whatever period of
interrelated so as to calculate a regression coefficient that can be used to calculate the
precipitation forecast based on historical data.
A form of regression coefficients is processed using Genetic Algorithms. The best form
of coefficients obtained by using the optimal parameters of genetic algorithms. Based on testing,
the optimal parameters obtained is population size of 50, generation number 50, appropriate
combinations of Cr and Mr value is 0.7 and 0.3. Using the optimal parameters of GAs, the best
non-linear coefficient is formed. The variables that influence for each location are different. For
Puspo area is using intercept, 12 variables of X dan 11 of X
2
. In Sumber also using intercept, 11
variables of X dan 11 for X
2
. Tosari area also using intercept, 17 variables of X and 18 variable
of X
2
. The last area is kept using intercept, variables 14 X dan 17 for X
2
.
The best precipitation prediction obtained by using a genetic algorithm with optimal
parameters of 4 locations in Tengger compared to precipitation prediction from previous studies
[1, 10]. Based on the RMSE value of each method on every location, precipitation using GAs-
Non-Linear Regression is better than Fuzzy Tsukamoto for each location. Compared to GSTAR-
SUR, precipitation prediction using GAs is better. This has been proved that for 3 locations GAs
is superior and on 1 location, GAs has the least value of deviation level. So that GAs is
recommended for solving precipitation prediction in different locations.
Our next study will consider building more powerful approach by hybridizing GAs with
another heuristic method to get a better model with lower RMSE. This hybrid approach has
been proven more effective for complex problems [24].
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