This document discusses using artificial intelligence techniques like fuzzy logic and adaptive neuro fuzzy inference systems (ANFIS) to model and predict urban water consumption time series data at different time scales (daily, weekly, monthly). It analyzes water usage data from New Mangalore Port in India to develop and compare prediction models using these methods as well as multiple linear regression. The results show that ANFIS models using Takagi-Sugeno inference performed best, outperforming fuzzy logic models and regression. The effects of varying the length of the input data set and different model structures are also investigated.
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.
Assessment of two Methods to study Precipitation PredictionAI Publications
Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Multiple imputation for hydrological missing data by using a regression metho...eSAT Journals
Abstract Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R2, and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data. Multiple r values for the Klang at Taman Sri Muda1, Klang at Jam, Sulaiman, WP and Klang at Emp Genting Klang, WP are 0.35, 0.42 and 0.28, respectively. Keywords: Filling gaps, Interpolation, Data mining, Statistical method, Rainfall, Water level
ASSESSMENT OF LP AND GA AS RESERVOIR SYSTEM ANALYSIS TOOLSIAEME Publication
A reservoir is a huge manmade structure constructed for a number of reasons. It
uses natural water resources and helps in the development of a society. The quantum
of water in a reservoir is a function of the hydrologic characteristics of the region. An
efficient planning and operation of a reservoir is a skill of the water planner. The
works done by researchers in the system analysis of a reservoir are discussed in the
present paper. The most appreciated linear programming (LP) and genetic algorithm
(GA) are studied in the context of system analysis of Urmodi Reservoir in
Maharashtra, India. The objective function is set to minimize the sum of the squared
irrigation demand deficit. Results show that these tools seem to be versatile in nature
and efficiently adopted for reservoir operation purpose.
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.
Assessment of two Methods to study Precipitation PredictionAI Publications
Presipitation analysis plays an important role in hydrological studies. In this study, using 50 years of rainfall data and ARIMA model, critical areas of Iran were determined. For this purpose, annual rainfall data of 112 different synoptic stations in Iran were gathered. To summarize, it could be concluded that: ARIMA model was an appropriate tool to forecast annual rainfall. According to obtained results from relative error, five stations were in critical condition. At 45 stations accrued rainfalls with amounts of less than half of average in the 50-year period. Therefore, in these 45 areas, chance of drought is more than other areas of Iran.
Application of mathematical modelling in rainfall forcast a csae study in...eSAT Journals
Abstract Malaysia receives rainfall from 2000 mm to 4000 mm annually where it is greatly influenced by two monsoon periods in November to March and May to September. The state of Sarawak is well known for its long and wide rivers. Numerous activities such as commercial, industrial and residential can always be found in the vicinity of the rivers. The activities have started since decades ago and still continue to grow and spatially expanding through times providing incomes ranging from small farmers to the largest corporations. Unfortunately, these areas are expected to experience frequent flood events as well as possible receding water level in rivers based on the findings of previous studies. If the projections are accurate, the productivity of these activities will be reduced, hence, in a longer term may affect the economy of the state as whole as well. Therefore, there is an urgent need for existing knowledge on rainfall behavior to be revised as effects of climate change with the intention that the state can fully utilize the favorable conditions and make scientific based decisions in the future. Recent study reveals that the Fourier series (FS), has the ability to simulate long-term rainfall up to 300 years is viewed as an important finding in the study of rainfall forecast. Long-term rainfall forecasting is viewed to be beneficial to the state of Sarawak in its future planning in various sectors such as water supply, flood mitigation, river transportation as well as agriculture. The main goal of the study is to apply a mathematical modeling in rainfall forecasting for the Sungai Sarawak basin. Data from eight rain gauge stations was analyzed and prepared for missing data, consistency check and adequacy of number of stations. Simple statistical analysis was conducted on the data such as maximum, minimum, mean and standard deviation. 27 years of annual rainfall data were simulated with the Fourier Series equation using spreadsheet. Hence, the result was compared with the Fitting N-term Harmonic Series. The model result reveals that the Fourier Series has the ability to simulate the observed data by being able to describe the rainfall pattern and there is a reasonable relationship between the simulation and observed data with p-value of 0.93. Keywords: Fourier series, Mathematical
Multiple imputation for hydrological missing data by using a regression metho...eSAT Journals
Abstract Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R2, and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data. Multiple r values for the Klang at Taman Sri Muda1, Klang at Jam, Sulaiman, WP and Klang at Emp Genting Klang, WP are 0.35, 0.42 and 0.28, respectively. Keywords: Filling gaps, Interpolation, Data mining, Statistical method, Rainfall, Water level
ASSESSMENT OF LP AND GA AS RESERVOIR SYSTEM ANALYSIS TOOLSIAEME Publication
A reservoir is a huge manmade structure constructed for a number of reasons. It
uses natural water resources and helps in the development of a society. The quantum
of water in a reservoir is a function of the hydrologic characteristics of the region. An
efficient planning and operation of a reservoir is a skill of the water planner. The
works done by researchers in the system analysis of a reservoir are discussed in the
present paper. The most appreciated linear programming (LP) and genetic algorithm
(GA) are studied in the context of system analysis of Urmodi Reservoir in
Maharashtra, India. The objective function is set to minimize the sum of the squared
irrigation demand deficit. Results show that these tools seem to be versatile in nature
and efficiently adopted for reservoir operation purpose.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Adequate knowledge about the hydrology is very much required for the proper planning and management of water resources in an area. Rainfall and runoff are the important constituents in determining the hydrology of an area to determine the water management strategies. SCS- CN method is a widely used method for the calculation of surface runoff considering the land use pattern, soil type and antecedent moisture condition. In the present study runoff of the Palar watershed, Karnataka state, South India has been calculated using the SCS-CN method. The watershed consists of a total area of 2872.357 km2. The maximum rainfall of 1231.67 mm in the year 2005 and a minimum of 418.7 mm in the year 2003. The average annual runoff is calculated as 218.26 mm and 626.91MCM. The rainfall- runoff correlation value is 0.8253. The study results can be effectively coordinated for the watershed management activities.
Estimation of Annual Runoff in Indravati Sub Basin of Godavari River using St...AM Publications
Prediction of runoff from known rainfall is one of the major problems confronted by hydrologists. There is lack of availability of long period runoff records in large number of catchments in India. Investigators have proposed many empirical relationships for runoff estimation in different catchments based on limited data of parameters affecting runoff. These regional relationships are useful in planning of water resource projects. This study was carried out to obtain simple yet effective relationship for estimation of annual runoff in Indravati sub basin of Godavari river. Regression analysis was carried out using annual rainfall, annual runoff and average annual temperature data to develop empirical models for annual runoff estimation. GIS software was used for preparing maps for the study area and to extract the precipitation and temperature data available in grid format from IMD. The best suited empirical model is then selected as per statistical criteria with lower values of standard error, standard deviation, mean absolute deviation (MAD), root mean square error (RMSE) and higher values of R square and correlation coefficient. Statistical significance of selected empirical model was evaluated by paired t test, F test and P value at 95 % confidence level. The developed relationship is then compared with the existing Khosla and Inglis and DeSouza relationships. Outcome of this comparison produces encouraging inferences to suggest an effective regional relationship for annual runoff estimation in the Indravati sub basin of Godavari river in India.
Assessing Water Demand And Supply For Srinagar City (J&K) India, Under Chang...IJMER
The study holds significance keeping in view the global climatic concerns, which began
to cast their shadows on the climate of Jammu and Kashmir as well. In order to accomplish the
present study, WEAP (water evaluation and planning model) of Stockholm Environment Institute
was used. This model is a tool for integrated water resource management and planning like,
forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas
and Hydropower generation, etc,. During the present study discharge data from 1979-2010 (past
thirty years) of our study rivers i.e., Dachigam Stream and Sindh Stream was used as supply to our
demand sites and also to find the impacts of changing climatic conditions over them. Due to
availability of data upto year 2010 only therefore the scenarios were generated from year 2011
onwards. The water demands for Srinagar i,e., irrigation demands for agriculture and water
supply demands for our domestic needs was analyzed, industrial demands were not analyzed as we
have negligible demands in this sector. The water supplied to our demand sites was mostly
contributed by our study rivers and a little demand was met by ground water. Data was collected
from various agencies like PHE Srinagar, Census data of 2011, Meteorology department etc. This
collected and generated data was given as input to the WEAP model. The model generated the
trends for discharge of our study rivers for next 15 years and at the same time also generated
scenarios calculating our demands and supplies for the future. The model results reveal that there
will be shortages in the requirements met in the urban water needs for some years like 2016, 2017,
2018 and 2020. The results generated from the model outputs will help us in predicting whether
our water resources are going to suffice our growing water needs or not in future. The results will
help in drafting policies for future regarding water supplies and demands under changing climatic
scenarios.
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.
Best Fit and Selection of Probability Distribution Models for Frequency Analy...IJERD Editor
Frequency analysis of extreme low mean annual rainfall events is important to water resource planners at catchment level because mean annual rainfall is an important parameter in determining mean annual runoff. Mean annual runoff is an important input in determining surface water available for water resource infrastructure development. In order to carry out frequency analysis of extreme low mean annual rainfall events, it is necessary to identify the best fit probability distribution models (PDMs) for the frequency analysis. The primary objective of the study was to develop two model identification criteria. The first criterion was developed to identify candidate probability distribution models from which the best fit probability distribution models were identified. The second criterion was applied to select the best fit probability distribution models from the candidate models. The secondary objectives were:
PERFORMANCE INDEX DEGRADATION MODEL OF SURFACE IRRIGATION SYSTEMIAEME Publication
This paper intends to investigate the service life time and to formulate a model of
performance index degradation of surface irrigation system. The study conducted in
all of technical surface irrigation area in Indonesia which has the assessment of
irrigation performance and the real value of operation and maintenance demand
during six months. The secondary data and site visit to location (primary data) are
used for analysis in this study. The methodology consists of analysis on the physical
aspects index that are physical infrastructure index and supporting aspect index. The
supporting aspect index consists of 5 parameters that are crop productivity,
supporting means, management organization, and institutional condition. Result
shows that performance index degradation of surface irrigation system is as the
addition between physical aspect and supporting aspect. The dominant parameter in
the supporting aspect is crop productivity
Optimization Analysis of Irrigation Water Using Linear Programpaperpublications3
Abstract: Irrigation water is a resource that is very strategic agriculture, the role of irrigation water has a very large dimensions. These resources not only affect productivity but also affects the spectrum utilization of agricultural commodities. Along with population growth, the demand for irrigation water to produce food (rice) will continue to increase. This is related to the fact that the setting and management of irrigation water are critical to improving agricultural productivity ..
Therefore we need a system of regulation and management of water resources so that irrigation water can be used optimally, including the provision of irrigation water that is tailored to their needs. Provision of irrigation water is the optimal amount of irrigation water supplied from the source through carrier channels (primary and secondary), tertiary canals, until the rice fields as needed.
In this study, the optimization is done by using a Linear Program. Value obtained from this optimization needs irrigation water as needed.
In addition to the optimization is done, to achieve high efficiency and the need for channel maintenance of existing irrigation facilities so not much irrigation water is wasted.
Surface Air Temperature of Kolkata District of West Bengal, India A Statistic...ijtsrd
The impact of steady increase in surface air temperature on climate change is a serious topic of today’s discussion. This study deals with the trend analysis and time series analysis and its future forecasts of surface air temperature of Kolkata district of West Bengal state, India during 1901 2002. The mean of annual average, maximum and minimum surface air temperature 0C of last 102 years are respectively 26.73, 31.26 and 22.23 with 1.27 , 1.28 and 1.73 coefficients of variation. A non parametric trend test namely the Mann Kendall MK Trend Test along with the Sen’s Slope estimator has been used to determine the monotonic trend of temperature and the magnitude of trend respectively. On the other hand, the Innovative Trend Test is also being performed for pattern determination. Both of the Mann Kendall MK Trend Test and Innovative Trend Test show that the trend of average, minimum and maximum surface air temperatures of Kolkata are increasing i.e. upwards in nature. The time series forecasts of surface air temperature are done by Autoregressive Integrated Moving Average ARIMA model. This upward trend of surface air temperature has an impact on human life and urbanization as well. The impact of climate change and related socio economic development will affect the ability of a nation to achieve sustainable development goals. The statistical analysis is carried out by using “R†Version 3.6.1 statistical software. Amartya Bhattacharya "Surface Air Temperature of Kolkata District of West Bengal, India - A Statistical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43684.pdf Paper URL: https://www.ijtsrd.comother-scientific-research-area/enviormental-science/43684/surface-air-temperature-of-kolkata-district-of-west-bengal-india--a-statistical-study/amartya-bhattacharya
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
Optical character recognition (OCR) is one of the active bases of sample detection topics. The current study focuses on automatic detection and recognition of hand written Farsi characters. For this purpose; we proposed two different methods based on neural networks and a special post processing approach to improve recognition rate of Farsi uppercase letters. In the first method, we extracted wavelet features from borders of character images and learned a neural network based these patterns. In the second method, we divided input characters into five groups according to the number of their components and used a set of appropriate moment features in each group and classified characters by the Bayesian rule. In a post-processing stage, some structural and statistical features were employed by a decision tree classifier to reduce the misrecognition rate. Our experimental results show suitable recognition rate for both methods.
Material didáctico elaborado polo CEIP Galán, de Oseiro (Arteixo). Inclúe actividades, textos, exercicios de comprensión, pasatempos, manualidades, ect.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Adequate knowledge about the hydrology is very much required for the proper planning and management of water resources in an area. Rainfall and runoff are the important constituents in determining the hydrology of an area to determine the water management strategies. SCS- CN method is a widely used method for the calculation of surface runoff considering the land use pattern, soil type and antecedent moisture condition. In the present study runoff of the Palar watershed, Karnataka state, South India has been calculated using the SCS-CN method. The watershed consists of a total area of 2872.357 km2. The maximum rainfall of 1231.67 mm in the year 2005 and a minimum of 418.7 mm in the year 2003. The average annual runoff is calculated as 218.26 mm and 626.91MCM. The rainfall- runoff correlation value is 0.8253. The study results can be effectively coordinated for the watershed management activities.
Estimation of Annual Runoff in Indravati Sub Basin of Godavari River using St...AM Publications
Prediction of runoff from known rainfall is one of the major problems confronted by hydrologists. There is lack of availability of long period runoff records in large number of catchments in India. Investigators have proposed many empirical relationships for runoff estimation in different catchments based on limited data of parameters affecting runoff. These regional relationships are useful in planning of water resource projects. This study was carried out to obtain simple yet effective relationship for estimation of annual runoff in Indravati sub basin of Godavari river. Regression analysis was carried out using annual rainfall, annual runoff and average annual temperature data to develop empirical models for annual runoff estimation. GIS software was used for preparing maps for the study area and to extract the precipitation and temperature data available in grid format from IMD. The best suited empirical model is then selected as per statistical criteria with lower values of standard error, standard deviation, mean absolute deviation (MAD), root mean square error (RMSE) and higher values of R square and correlation coefficient. Statistical significance of selected empirical model was evaluated by paired t test, F test and P value at 95 % confidence level. The developed relationship is then compared with the existing Khosla and Inglis and DeSouza relationships. Outcome of this comparison produces encouraging inferences to suggest an effective regional relationship for annual runoff estimation in the Indravati sub basin of Godavari river in India.
Assessing Water Demand And Supply For Srinagar City (J&K) India, Under Chang...IJMER
The study holds significance keeping in view the global climatic concerns, which began
to cast their shadows on the climate of Jammu and Kashmir as well. In order to accomplish the
present study, WEAP (water evaluation and planning model) of Stockholm Environment Institute
was used. This model is a tool for integrated water resource management and planning like,
forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas
and Hydropower generation, etc,. During the present study discharge data from 1979-2010 (past
thirty years) of our study rivers i.e., Dachigam Stream and Sindh Stream was used as supply to our
demand sites and also to find the impacts of changing climatic conditions over them. Due to
availability of data upto year 2010 only therefore the scenarios were generated from year 2011
onwards. The water demands for Srinagar i,e., irrigation demands for agriculture and water
supply demands for our domestic needs was analyzed, industrial demands were not analyzed as we
have negligible demands in this sector. The water supplied to our demand sites was mostly
contributed by our study rivers and a little demand was met by ground water. Data was collected
from various agencies like PHE Srinagar, Census data of 2011, Meteorology department etc. This
collected and generated data was given as input to the WEAP model. The model generated the
trends for discharge of our study rivers for next 15 years and at the same time also generated
scenarios calculating our demands and supplies for the future. The model results reveal that there
will be shortages in the requirements met in the urban water needs for some years like 2016, 2017,
2018 and 2020. The results generated from the model outputs will help us in predicting whether
our water resources are going to suffice our growing water needs or not in future. The results will
help in drafting policies for future regarding water supplies and demands under changing climatic
scenarios.
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.
Best Fit and Selection of Probability Distribution Models for Frequency Analy...IJERD Editor
Frequency analysis of extreme low mean annual rainfall events is important to water resource planners at catchment level because mean annual rainfall is an important parameter in determining mean annual runoff. Mean annual runoff is an important input in determining surface water available for water resource infrastructure development. In order to carry out frequency analysis of extreme low mean annual rainfall events, it is necessary to identify the best fit probability distribution models (PDMs) for the frequency analysis. The primary objective of the study was to develop two model identification criteria. The first criterion was developed to identify candidate probability distribution models from which the best fit probability distribution models were identified. The second criterion was applied to select the best fit probability distribution models from the candidate models. The secondary objectives were:
PERFORMANCE INDEX DEGRADATION MODEL OF SURFACE IRRIGATION SYSTEMIAEME Publication
This paper intends to investigate the service life time and to formulate a model of
performance index degradation of surface irrigation system. The study conducted in
all of technical surface irrigation area in Indonesia which has the assessment of
irrigation performance and the real value of operation and maintenance demand
during six months. The secondary data and site visit to location (primary data) are
used for analysis in this study. The methodology consists of analysis on the physical
aspects index that are physical infrastructure index and supporting aspect index. The
supporting aspect index consists of 5 parameters that are crop productivity,
supporting means, management organization, and institutional condition. Result
shows that performance index degradation of surface irrigation system is as the
addition between physical aspect and supporting aspect. The dominant parameter in
the supporting aspect is crop productivity
Optimization Analysis of Irrigation Water Using Linear Programpaperpublications3
Abstract: Irrigation water is a resource that is very strategic agriculture, the role of irrigation water has a very large dimensions. These resources not only affect productivity but also affects the spectrum utilization of agricultural commodities. Along with population growth, the demand for irrigation water to produce food (rice) will continue to increase. This is related to the fact that the setting and management of irrigation water are critical to improving agricultural productivity ..
Therefore we need a system of regulation and management of water resources so that irrigation water can be used optimally, including the provision of irrigation water that is tailored to their needs. Provision of irrigation water is the optimal amount of irrigation water supplied from the source through carrier channels (primary and secondary), tertiary canals, until the rice fields as needed.
In this study, the optimization is done by using a Linear Program. Value obtained from this optimization needs irrigation water as needed.
In addition to the optimization is done, to achieve high efficiency and the need for channel maintenance of existing irrigation facilities so not much irrigation water is wasted.
Surface Air Temperature of Kolkata District of West Bengal, India A Statistic...ijtsrd
The impact of steady increase in surface air temperature on climate change is a serious topic of today’s discussion. This study deals with the trend analysis and time series analysis and its future forecasts of surface air temperature of Kolkata district of West Bengal state, India during 1901 2002. The mean of annual average, maximum and minimum surface air temperature 0C of last 102 years are respectively 26.73, 31.26 and 22.23 with 1.27 , 1.28 and 1.73 coefficients of variation. A non parametric trend test namely the Mann Kendall MK Trend Test along with the Sen’s Slope estimator has been used to determine the monotonic trend of temperature and the magnitude of trend respectively. On the other hand, the Innovative Trend Test is also being performed for pattern determination. Both of the Mann Kendall MK Trend Test and Innovative Trend Test show that the trend of average, minimum and maximum surface air temperatures of Kolkata are increasing i.e. upwards in nature. The time series forecasts of surface air temperature are done by Autoregressive Integrated Moving Average ARIMA model. This upward trend of surface air temperature has an impact on human life and urbanization as well. The impact of climate change and related socio economic development will affect the ability of a nation to achieve sustainable development goals. The statistical analysis is carried out by using “R†Version 3.6.1 statistical software. Amartya Bhattacharya "Surface Air Temperature of Kolkata District of West Bengal, India - A Statistical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd43684.pdf Paper URL: https://www.ijtsrd.comother-scientific-research-area/enviormental-science/43684/surface-air-temperature-of-kolkata-district-of-west-bengal-india--a-statistical-study/amartya-bhattacharya
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
Optical character recognition (OCR) is one of the active bases of sample detection topics. The current study focuses on automatic detection and recognition of hand written Farsi characters. For this purpose; we proposed two different methods based on neural networks and a special post processing approach to improve recognition rate of Farsi uppercase letters. In the first method, we extracted wavelet features from borders of character images and learned a neural network based these patterns. In the second method, we divided input characters into five groups according to the number of their components and used a set of appropriate moment features in each group and classified characters by the Bayesian rule. In a post-processing stage, some structural and statistical features were employed by a decision tree classifier to reduce the misrecognition rate. Our experimental results show suitable recognition rate for both methods.
Material didáctico elaborado polo CEIP Galán, de Oseiro (Arteixo). Inclúe actividades, textos, exercicios de comprensión, pasatempos, manualidades, ect.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
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.
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
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.
Disaggregation of Annual to daily Streamflows: A lineardeterministic methodIOSRJAP
In this study, a linear deterministic methodis applied to disaggregate streamflow from annual to daily data inunregulated stations located on the Kızılırmak river in Turkey. To disaggregate annual streamflows to the daily flow at the target station (TS), annual counterparts at the source station (SS) were identified depending on the minimum error criteria that is estimated based on the volume of three-year time window. Then, daily streamflow indexes at SS were calculated to disaggregate annual to daily streamflow at TS through the process. The same steps are replicated to disaggregate monthly streamflow to the daily flow for the purpose of comparison between the two methods. The results are well represents daily streamflow at two methods inquiry comparing to observe data, and also maintain the time series statistical characteristics and mass equilibrium. The comparative results suggest that the monthly to daily disaggregation method perform better than annual to the daily disaggregation method. The daily streamflow generated in this study can be used in the future research for water resources planning and management.
DEVELOPMENT OF CLEAN WATER DISTRIBUTION NETWORK CAPACITY BY USING WATERCADIAEME Publication
In this study a network model was constructed for the hydraulic analysis and
design of a small community (Kedungkandang District) water distribution network in
East Java Province of Indonesia by using Water cad simulator. The analysis included
a review of pressures, velocities and head loss gradients under steady state average
day need. The clean water availability in the location study is 560 l/s, however the
local society that is 23,213 consumers can only use in amount of 116 l/s. The
assessment of existing condition due to the pipe hydraulic condition and the
development of capacity network increasing are carried out by using the program of
Water cad vs. XM Edition. The development condition consists of 27,284 populations.
Result indicates that the average discharge need is 41.763 l/s, however in the peak
hour need there is needed 65.150 l/s on 2031. The water pressure in the development
area is 2.3 atm on 06.00 am
Statistical analysis of an orographic rainfall for eight north-east region of...IJICTJOURNAL
Autoregressive integrated moving average (ARIMA) models are used to predict the rain rate for orographic rainfall over a long period of time, from 1980 to 1918. As the orographic rainfall may cause landslides and other natural disaster issues, So, this study is very important for the analysis of rainfall prediction. In this research, statistical calculations have been done based on the rainfall data for twelve regions of India (Cherrapunji, Darjeling, Dawki, Ghum, Itanagar, Kamchenjunga, Mizoram, Nagaland, Pakyong, Saser Kangri, Slot Kangri, and Tripura) from the eight states, i.e., Sikkim, Meghalaya, West Bengal, Ladakh (Union Territory of India), Arunachal Pradesh, Mizoram, Tripura, and Nagaland) with varying altitude. The model's output is assessed using several error calculations. The model's performance is represented by the fit value, which is reliable for the northeast region of India with increasing altitude. The statistical dependability of the rainfall prediction is shown by the parameters. The lowest value of root mean square error (RMSE) indicates better prediction for orographic rainfall.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
ASSESSING THE EFFECTS OF SPATIAL INTERPOLATION OF RAINFALL ON THE STREAMFLOW ...civej
Precipitation within a river basin varies spatially and temporally and hence, is the most relevant input for
hydrologic modelling. Various interpolation methods exist to distribute rainfall spatially within a basin.
The sparse distribution of raingauge stations within a river basin and the differences in interpolation
methods can potentially impact the streamflow simulated using a hydrologic model. The present study
focuses on assessing the effect of spatial interpolation of rainfall using Theissen polygon, Inverse distance
weighted (IDW) method and Ordinary Kriging on the streamflow simulated using a physically based
spatially distributed model-SHETRAN in Vamanapuram river basin in Southern Kerala, India. The
SHETRAN model in the present study utilises rainfall data from the available rain gauge stations within the
basin and potential evapo-transpiration calculated using Penman-Monteith method, along with other input
parameters like soil and landuse. Four years of rainfall and evapo-transpiration data on a daily scale is
used for model calibration and one year data for validation. The performance of the different spatial
interpolation methods were assessed based on the Mean Annual flow and statistical parameters like NashSutcliffe
Efficiency, coefficient of determination. The ordinary kriging and IDW methods were found to be
satisfactory in the spatial interpolation of rainfall.
Tech transfer making it as a risk free approach in pharmaceutical and biotech iniaemedu
Tech transfer is a common methodology for transferring new products or an existing
commercial product to R&D or to another manufacturing site. Transferring product knowledge to the
manufacturing floor is crucial and it is an ongoing approach in the pharmaceutical and biotech
industry. Without adopting this process, no company can manufacture its niche products, let alone
market them. Technology transfer is a complicated, process because it is highly cross functional. Due
to its cross functional dependence, these projects face numerous risks and failure. If anidea cannot be
successfully brought out in the form of a product, there is no customer benefit, or satisfaction.
Moreover, high emphasis is in sustaining manufacturing with highest quality each and every time. It
is vital that tech transfer projects need to be executed flawlessly. To accomplish this goal, risk
management is crucial and project team needs to use the risk management approach seamlessly.