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.
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.
International Telecommunication Union-Radiocommunication Sector P.837-6 and P...TELKOMNIKA JOURNAL
This work evaluated the performance of International Telecommunication Union-Radiocommunication Sector (ITU-R) P.837-6 and P.837-7 models (Annex 1) to estimate one-minute rainfall rates in Indonesia. In addition to the default ITU-R P.837-6, the input of ITU-R P.837-6 is also modified using data which has better spatial resolution, i.e. a combination of Tropical Rainfall Measuring Mission (TRMM) 3A25 and 3B43 (ITU-R+3A25+3B43), 3B42 and 3B43 (ITU-R+3B42+3B43), Global Satellite Mapping of Precipitation (ITU-R+GSMaP), and Global Precipitation Measurement (ITU-R+GPM). Among the five test sites, the default ITU-R P.837-6 and ITU-R+3A25+3B43 could predict one-minute rainfall rates at two locations accurately. The ITU-R P.837-7 exhibited a marginally better performance for sites that had a high percentage of very heavy rain, particularly at large (1%) and small (0.001%) percentages of time exceeded. The spatial distribution of rainfall rate produced by ITU-R P.837-7 and ITU-R+3A25+3B43 was closer to the pattern demonstrated by recent satellite precipitation measurements.
Estimation of TRMM rainfall for landslide occurrences based on rainfall thres...IJECEIAES
Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.
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.
International Telecommunication Union-Radiocommunication Sector P.837-6 and P...TELKOMNIKA JOURNAL
This work evaluated the performance of International Telecommunication Union-Radiocommunication Sector (ITU-R) P.837-6 and P.837-7 models (Annex 1) to estimate one-minute rainfall rates in Indonesia. In addition to the default ITU-R P.837-6, the input of ITU-R P.837-6 is also modified using data which has better spatial resolution, i.e. a combination of Tropical Rainfall Measuring Mission (TRMM) 3A25 and 3B43 (ITU-R+3A25+3B43), 3B42 and 3B43 (ITU-R+3B42+3B43), Global Satellite Mapping of Precipitation (ITU-R+GSMaP), and Global Precipitation Measurement (ITU-R+GPM). Among the five test sites, the default ITU-R P.837-6 and ITU-R+3A25+3B43 could predict one-minute rainfall rates at two locations accurately. The ITU-R P.837-7 exhibited a marginally better performance for sites that had a high percentage of very heavy rain, particularly at large (1%) and small (0.001%) percentages of time exceeded. The spatial distribution of rainfall rate produced by ITU-R P.837-7 and ITU-R+3A25+3B43 was closer to the pattern demonstrated by recent satellite precipitation measurements.
Estimation of TRMM rainfall for landslide occurrences based on rainfall thres...IJECEIAES
Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.
Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regres...journalBEEI
Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.
Binary classification of rainfall time-series using machine learning algorithmsIJECEIAES
Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.
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
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
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.
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.
A framework for cloud cover prediction using machine learning with data imput...IJECEIAES
The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.
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
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.
Generation of intensity_duration_frequency_curves_for manvi taluk raichur dis...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Manvi Taluk of Raichur District using four Rain gauge Station with rain gauge stations with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution techniques are used to derived the rainfall intensity values of 2,5,10,15,30,60,120,720,1440 minutes of rainfall duration with different return period. The short duration IDF using daily rainfall data are presented, which is input for water resources projects.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The most critical parameters that indicate the Wi-Fi network are throughput, delay, latency, and packet loss since they provide significant benefits, especially to the end-user. This research aims to investigate Wi-Fi performance in an indoor environment for light-of-sight (LOS) and nonlight-of-sight (NLOS) conditions. The effect of the surrounding obstacles and distance has also been reported in the paper. The parameters measured are packet loss, the packet sent, the packet received, throughput, and latency. Site measurement is done to obtain real-time and optimum results. The measured parameters are then validated using the EMCO ping monitor 8 software. The comparison results between the measurement and the simulation are well presented in this paper. Additionally, the measurement distance is done up to 30 meters and the results are reported in the paper as well. The results indicate that the throughput value decreases with an increasing distance, where the lowest throughput value is 24.64 Mbps and the highest throughput value is 70.83 Mbps. Next, the maximum latency value from the measurement is 79 ms, while the lowest latency value is 56.09 ms. Finally, this research verified that obstacles and distances are among the contributing factors affecting the throughput and latency performance of the Wi-Fi network.
Subarrays of phased-array antennas for multiple-input multiple-output radar a...IJICTJOURNAL
The subarray MIMO radar (SMIMO) is a multiple-input multiple-output (MIMO) radar with elements in the form of a sub-array that acts as a phased array (PAR), so it combines at the same time the key advantage of the PAR radar, which is high directional gain to increase target range, and the key advantage of the MIMO radar, i.e., high diversity gains to increase the maximum number of detected targets. Different schemes for the number of antenna elements in the transceiver zones, such as uniform and/or variable, overlapped and non-overlapped, significantly determine the performance of radars as virtual arrays (VARs), maximum number of detected targets, accuracy of target angle, detection resolution, SNR detection, and detection probability. Performance is also compared with the PAR, the MIMO, and the phased MIMO radars (PMIMO). The SMIMO radar offers great versatility for radar applications, being able to adapt to different shapes of the multiple targets to be detected and their environment. For example, for a transmit-receive with an antenna element number, i.e., M = N = 8, the range of the number of detected targets for the SMIMO radar is flexible compared to the other radars. On the other hand, the proposed radar's signal-to-noise ratio (SNR) detection performance and detection probability (K = 5, L = 3) are both 1,999 and above 90%, which are better than other radars.
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Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regres...journalBEEI
Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.
Binary classification of rainfall time-series using machine learning algorithmsIJECEIAES
Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.
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
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
Utilitas Mathematica Journal has become a fully open-access journal. This journal publishes mainly in areas of pure and applied mathematics, Statistics. Our journal is an official publication of the Utilitas mathematical journal’s original research articles and aspects of both pure and applied mathematics.
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.
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.
A framework for cloud cover prediction using machine learning with data imput...IJECEIAES
The climatic conditions of a region are affected by multiple factors. These factors are dew point temperature, humidity, wind speed, and wind direction. These factors are closely related to each other. In this paper, the correlation between these factors is studied and an approach has been proposed for data imputation. The idea is to utilize all these features to obtain the prediction of the total cloud cover of a region instead of removing the missing values. Total cloud cover prediction is significant because it affects the agriculture, aviation, and energy sectors. Based on the imputed data which is obtained as the output of the proposed method, a machine learning-based model is proposed. The foundation of this proposed model is the bi-directional approach of the long short-term memory (LSTM) model. It is trained for 8 stations for two different approaches. In the first approach, 80% of the entire data is considered for training and 20% of the data is considered for testing. In the second approach, 90% of the entire data is accounted for training and 10% of the data is accounted for testing. It is observed that in the first approach, the model gives less error for prediction.
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
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.
Generation of intensity_duration_frequency_curves_for manvi taluk raichur dis...Mohammed Badiuddin Parvez
The estimation of rainfall intensity is commonly required for the design of hydraulic and water resources engineering control structures. The intensity-duration-frequency (IDF) relationship is a mathematical relationship between the rainfall intensity, the duration and the return period. The present study aimed the derivation of IDF curves of Manvi Taluk of Raichur District using four Rain gauge Station with rain gauge stations with 19 years of rainfall data (1998 to 2016). The Normal Distribution, Log Normal Distribution, Gumbel distribution techniques are used to derived the rainfall intensity values of 2,5,10,15,30,60,120,720,1440 minutes of rainfall duration with different return period. The short duration IDF using daily rainfall data are presented, which is input for water resources projects.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The most critical parameters that indicate the Wi-Fi network are throughput, delay, latency, and packet loss since they provide significant benefits, especially to the end-user. This research aims to investigate Wi-Fi performance in an indoor environment for light-of-sight (LOS) and nonlight-of-sight (NLOS) conditions. The effect of the surrounding obstacles and distance has also been reported in the paper. The parameters measured are packet loss, the packet sent, the packet received, throughput, and latency. Site measurement is done to obtain real-time and optimum results. The measured parameters are then validated using the EMCO ping monitor 8 software. The comparison results between the measurement and the simulation are well presented in this paper. Additionally, the measurement distance is done up to 30 meters and the results are reported in the paper as well. The results indicate that the throughput value decreases with an increasing distance, where the lowest throughput value is 24.64 Mbps and the highest throughput value is 70.83 Mbps. Next, the maximum latency value from the measurement is 79 ms, while the lowest latency value is 56.09 ms. Finally, this research verified that obstacles and distances are among the contributing factors affecting the throughput and latency performance of the Wi-Fi network.
Subarrays of phased-array antennas for multiple-input multiple-output radar a...IJICTJOURNAL
The subarray MIMO radar (SMIMO) is a multiple-input multiple-output (MIMO) radar with elements in the form of a sub-array that acts as a phased array (PAR), so it combines at the same time the key advantage of the PAR radar, which is high directional gain to increase target range, and the key advantage of the MIMO radar, i.e., high diversity gains to increase the maximum number of detected targets. Different schemes for the number of antenna elements in the transceiver zones, such as uniform and/or variable, overlapped and non-overlapped, significantly determine the performance of radars as virtual arrays (VARs), maximum number of detected targets, accuracy of target angle, detection resolution, SNR detection, and detection probability. Performance is also compared with the PAR, the MIMO, and the phased MIMO radars (PMIMO). The SMIMO radar offers great versatility for radar applications, being able to adapt to different shapes of the multiple targets to be detected and their environment. For example, for a transmit-receive with an antenna element number, i.e., M = N = 8, the range of the number of detected targets for the SMIMO radar is flexible compared to the other radars. On the other hand, the proposed radar's signal-to-noise ratio (SNR) detection performance and detection probability (K = 5, L = 3) are both 1,999 and above 90%, which are better than other radars.
A broadband MIMO antenna's channel capacity for WLAN and WiMAX applicationsIJICTJOURNAL
This paper describes the findings of a research into the multiple input multiple output (MIMO) channel capacity of a broadband dual-element printed inverted F-antenna (PIFA) antenna array. The dual-element antenna array is made up of two PIFAs that are meant to fit on a teeny-tiny and small wireless communication device that runs at 5 GHz. The device's frequency range is between 3.5 and 4.5 GHz. These PIFAs are also loaded into the device during the installation process. In order to investigate the channel capacity, the ray tracing method is employed in two different kinds of circumstances. For the purpose of carrying out this analysis of the channel capacity, both the simulated and measured mutual couplings of the broadband MIMO antenna are utilized.
Satellite dish antenna control for distributed mobile telemedicine nodesIJICTJOURNAL
The positioning control of a dish antenna mounted on distributed mobile telemedicine nodes (DMTNs) within Nigeria communicating via NigComSat-1R has been presented. It was desired to improve the transient and steady performance of satellite dish antenna and reduce the effect of delay during satellite communication. In order to overcome this, the equations describing the dynamics of the antenna positioning system were obtained and transformed into state space variable equations. A full state feedback controller was developed with forward path gain and an observer. The proposed controller was introduced into the closed loop of the dish antenna positioning control system. The system was subjected to unit step forcing function in MATLAB/Simulink simulation environment considering three different cases so as to obtain time domain parameters that characterized the transient and steady state response performances. The simulation results obtained revealed that the introduction of the full state feedback controller provided improved position tracking to unit step input with a rise time of 0.42 s, settling time of 1.22 s and overshoot of 4.91%. With the addition of observer, the rise time achieved was 0.39 s, settling time of 1.31 s, and overshoot of 10.7%. The time domain performance comparison of the proposed system with existing systems revealed its superiority over them.
High accuracy sensor nodes for a peat swamp forest fire detection using ESP32...IJICTJOURNAL
The use of smoke sensors in high-precision and low-cost forest fire detection kits needs to be developed immediately to assist the authorities in monitoring forest fires especially in remote areas more efficiently and systematically. The implementation of automatic reclosing operation allows the fire detector kit to distinguish between real smoke and non-real smoke successfully. This has profitably reduced kit errors when detecting fires and in turn prevent the users from receiving incorrect messages. However, using a smoke sensor with automatic reclosing operation has not been able to optimize the accuracy of identifying the actual smoke due to the working sensor node situation is difficult to predict and sometimes unexpected such as the source of smoke received. Thus, to further improve the accuracy when detecting the presence of smoke, the system is equipped with two digital cameras that can capture and send pictures of fire smoke to the users. The system gives the users choice of three interesting options if they want the camera to capture and send pictures to them, namely request, smoke trigger and movement for security purposes. In all cases, users can request the system to send pictures at any time. The system equipped with this camera shows the accuracy of smoke detection by confirming the actual smoke that has been detected through images sent in the user’s Telegram channel and on the graphical user interface (GUI) display. As a comparison of the system before and after using this camera, it was found that the system that uses the camera gives advantage to the users in monitoring fire smoke more effectively and accurately.
Prediction analysis on the pre and post COVID outbreak assessment using machi...IJICTJOURNAL
In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
Meliorating usable document density for online event detectionIJICTJOURNAL
Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.
Performance analysis on self organization based clustering scheme for FANETs ...IJICTJOURNAL
With the fast-increasing development of wireless communication networks, unmanned aerial vehicle (UAV) has emerged as a flying platform for wireless communication with efficient coverage, capacity, reliability, and its network is called flying ad-hoc network (FANET); which keeps changing its topology due to its dynamic nature, causing inefficient communication, and therefore needs cluster formation. In this paper, we proposed a cluster formation, selection of cluster head and its members, connectivity and transmission with the base station using the K-means algorithm, and choice of an optimized path for transmission using firefly optimization algorithm for efficient communication. Evaluation of performance with experimental results are obtained and compared using the K-means algorithm and firefly optimization algorithm in cluster building time, cluster lifetime, energy consumption, and probability of delivery success. On comparison of firefly optimization algorithm with firefly optimization algorithm, i.e., K-means algorithm results proved than without firefly optimization algorithm, better in terms of cluster building time, energy consumption, cluster lifetime, and also the probability of delivery success.
A persuasive agent architecture for behavior change interventionIJICTJOURNAL
A persuasive agent makes use of persuasion attributions to ensure that its predefined objective(s) is achieved within its immediate environment. This is made possible based on the five unique features namely sociable, persuasive, autonomy, reactive, and proactive natures. However, there are limited successes recorded within the behavioural intervention and psychological reactance is responsible for these failures. Psychological reactance is the stage where rejection, negative response and frustration are felt by the users of the persuasive system. Thus, this study proposes a persuasive agent (PAT) architecture that limits the experience of psychological reactance to achieve an improved behavioural intervention. PAT architecture adopted the combination of the reactance model for behavior change and the persuasive design principle. The architecture is evaluated by conducting an experimental study using a user-centred approach. The evaluation reflected that there is a reduction in the number of users who experienced psychological reactance from 70 per cent to 3 per cent. The result is a better improvement compared with previous outcomes. The contribution made in this study would provide a design model and a steplike approach to software designers on how to limit the effect of psychological reactance on persuasive system applications and interventions.
Enterprise architecture-based ISA model development for ICT benchmarking in c...IJICTJOURNAL
Building on a coincided in progress paper, this paper constructs and evaluate an information systems architecture (ISA) model for the Bahraini architecture, engineering and construction (AEC) sector, from the lens of enterprise architecture (EA). This model acts as an information and communication technology (ICT) barometer tool to identify and benchmark the ICT’s gaps, duplicative levels, and future investments. Following the design science research, this paper and throughout a utilization of a tailored version of the open group architectural framework (TOGAF), embedded into a rigorous case study approach, the construction, testing, and evaluation of the conceptual ISA model is approached to benchmark the ICT measurement. Empirically, the study revealed the appropriateness of the model and the ability to identify the availability of 28 groups of 38 individual ICT applications in the Bahraini AEC sector and benchmark them to score an average of 18.5% against 17 countries that scored an average of 18.6%.
Empirical studies on the effect of electromagnetic radiation from multiple so...IJICTJOURNAL
Just after the invention of electricity by Michael Faraday, there has been a revolution in the communication technology, which lead to the invention of radio, television, radar, satellite, and mobile. While these machines transformed our life high quality, safer and simpler, they have been associated with alarming probable health hazards owing to their electromagnetic radiation (EMR) emission. Couple of cases it has been reported by personals regarding various health related issues relating to exposure on electromagnetic field (EMF) and EMR. Although couple of persons showed light symptoms and respond by avoiding the electrical field (EF) and EMR fields as much as possible, some others are so much affected that they have changed their entire lifestyle. In this paper, empirical survey study has been carried out in the laboratories of Daffodil International University (DIU) main and permanent campus. It was found that some of the instrument had higher EMFs. The findings from this survey may be helpful for the students to take precautionary measurement who work for long duration in the various laboratories for their practical classes and for the users of the domestic appliances as well as office equipment and industrial instruments.
Cyber attack awareness and prevention in network securityIJICTJOURNAL
This article aims to provide an overview of cyber attack awareness and prevention in network security. This article discussed the different types of cyber attacks, current trends of cyber attacks, how to prevent cyber attacks and uum students' awareness of cyber attacks. First, we will go over the different types of cyber attack, current trend, impact of cyber attack and the prevention. The approach entailed comparing and observing the outcomes of 13 different papers. The survey's findings would demonstrate the results obtained after analyzing the data collection which are the questionnaire filled out by respondents after watching the cyber attack awareness video to improve awareness of students through the cyber attack. Depending on the outcome of this survey, we will have a better understanding of current students' knowledge and awareness of cyber attacks, allowing us to improve students' understanding of cyber threats and the necessity of cyber security.
An internet of things-based irrigation and tank monitoring systemIJICTJOURNAL
Agriculture plays a significant role in the development of a nation and provides the main source of food production, income, and employment to nations. It was the most practiced occupation in Nigeria and this formed the backbone of the economy in the early 1960s before the discovery of crude oil, which has led to the derail of sufficient food production, exportation, and the agricultural economy at large. Over time, the dry season has always been challenging with little or no rainfall and there are no irrigation facilities that incorporate different saving practices to adapt to these climate changes on their own. In this paper, a cost-effective internet of things irrigation system that is capable of reducing water wastage, manual labor, monitoring tank water level and that can be controlled remotely is designed. The system integrated Arduino UNO with a soil moisture sensor, HCSR04 ultrasonic sensor, and ESP8266 Wi-Fi module that gives the system capable of being controlled remotely via the internet, thus achieving optimal irrigation using the internet of things (IoT). Some of the challenges facing the existing irrigation system are water wastage, poor performance, and high cost of implementation. The design system helps to control water supply to crops when it is needed, and also monitors soil moisture, temperature, and water tank level. After carrying out the experiments for 15 days, the system saved approximately 49% of the water used in traditional irrigation method. The system is useful in large farming areas to minimize human effort and reduce the cost of hiring personnel.
About decentralized swarms of asynchronous distributed cellular automata usin...IJICTJOURNAL
This research describes the simple implementation of asynchronous distributed cellular automata and decentralized swarms of asynchronous distributed cellular automata built on top of inter-planetary file system’s publish-subscribe (IPFS PubSub) experimentation. Various publish-subscribe(PubSub) models are described. As an illustration, two distributed versions and a decentralized swarm version of a 2D elementary cellular automaton are thoroughly detailed to highlight the simplicity of implementation with IPFS and the inner workings of these kinds of cellular automata (CA). Both algorithms were implemented, and experiments were conducted throughout five datacenters of Grid’5000 testbed in France to obtain preliminary performance results in terms of network bandwidth usage. This work is prior to implementing a large-scale decentralized epidemic propagation modeling and prediction system based upon asynchronous distributed cellular automata with application to the current pandemic of SARSCoV-2 coronavirus disease 2019 (COVID-19).
A convolutional neural network for skin cancer classificationIJICTJOURNAL
Skin diseases can be seen clearly by oneself and others. Although this disease is visible on the skin, we fear that this skin disease is harmful. People who experience skin diseases immediately visit a dermatologist to have their complaints and symptoms checked. This skin protects the body, especially from the sun, so it can be lethal if something goes wrong. One example of deadly skin disease is skin cancer or skin tumors. In this research, we classified skin cancer into Benign and Malignant using the convolution neural network (CNN) algorithm. The purpose of this research is to develop the CNN architecture to help identify skin diseases. We used a dataset of 3,297 skin cancer images which are publicly available on the Kaggle website. We propose two CNN architectures that differ in the number of parameters. The first architecture has 6,427,745 parameters, and the second architecture has 2,797,665. The accuracy of the proposed models is 93% and 74% respectively. The first model with the number of parameters 6,427,745 was saved for use in the creation of the website. We created a web-based application with the Django framework for skin disease identification.
A review on notification sending methods to the recipients in different techn...IJICTJOURNAL
Women have progressed a lot in terms of social empowerment and economics. They are going for higher education, jobs, and many other similar endeavors, but harassment cases have also been on the rise. So, women’s safety is a big concern nowadays, especially in developing countries. Many previous studies and attempts were made to create a feasible safety solution for women. Out of various features to ensure women’s safety in critical situations, location tracking is a very common and key feature in most previously proposed solutions. This study found mechanisms of sending the location to different types of recipients in various women safety solutions. In addition, the advantages and drawbacks of location sending methods in women's safety solutions were analyzed.
Detection of myocardial infarction on recent dataset using machine learningIJICTJOURNAL
In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and efficiency of the existing model. Additional parameters are used to predict MI with more accuracy. The proposed model is used to predict an early diagnosis of MI with the help of expertise experiences and data gathered from hospitals.
Correcting optical character recognition result via a novel approachIJICTJOURNAL
Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
Multiple educational data mining approaches to discover patterns in universit...IJICTJOURNAL
This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.
A novel enhanced algorithm for efficient human trackingIJICTJOURNAL
Tracking moving objects has been an issue in recent years of computer vision and image processing and human tracking makes it a more significant challenge. This category has various aspects and wide applications, such as autonomous deriving, human-robot interactions, and human movement analysis. One of the issues that have always made tracking algorithms difficult is their interaction with goal recognition methods, the mutable appearance of variable aims, and simultaneous tracking of multiple goals. In this paper, a method with high efficiency and higher accuracy was compared to the previous methods for tracking just objects using imaging with the fixed camera is introduced. The proposed algorithm operates in four steps in such a way as to identify a fixed background and remove noise from that. This background is used to subtract from movable objects. After that, while the image is being filtered, the shadows and noises of the filmed image are removed, and finally, using the bubble routing method, the mobile object will be separated and tracked. Experimental results indicated that the proposed model for detecting and tracking mobile objects works well and can improve the motion and trajectory estimation of objects in terms of speed and accuracy to a desirable level up to in terms of accuracy compared with previous methods.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
Statistical analysis of an orographic rainfall for eight north-east region of India with special focus over Sikkim
1. International Journal of Informatics and Communication Technology (IJ-ICT)
Vol. 11, No. 3, December 2022, pp. 185~193
ISSN: 2252-8776, DOI: 10.11591/ijict.v11i3.pp185-193 185
Journal homepage: http://ijict.iaescore.com
Statistical analysis of an orographic rainfall for eight north-east region of
India with special focus over Sikkim
Pooja Verma1
, Amrita Biswas1
, Swastika Chakraborty2
1
Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Rangpo, India
2
Department of Electronics and Communication Engineering, Narula Institute of Technology, Kolkata, India
Article Info ABSTRACT
Article history:
Received Mar 1, 2022
Revised Jul 22, 2022
Accepted Aug 8, 2022
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 north-
east 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.
Keywords:
ARIMA
Mean square error
Orographic rainfall
Rain-rate
RMSE
This is an open access article under the CC BY-SA license.
Corresponding Author:
Pooja Verma
Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology
Sikkim Manipal University
Majitar, Rangpo, Sikkim, India
Email: puja20verma@gmail.com
1. INTRODUCTION
The Orographic rainfall is characterized as widespread rainfall having rain rate in between 25 mm/hr
to 60 mm/hr. This type of rainfall has specific values of the parameter A=300 to 350 and b=0.5 to 0.7 in the
popular rainfall-radar reflectivity (Z-R) relation Z=𝐴 𝑟𝑏
. This type of rainfall is formed when a low cloud
approximate value of 0.85 to 0.9 accompanied by a wind-gust of 6 to 7 km/hr causes rainfall almost 70% of
the time during a year over the hills.
At frequencies above 10 GHz, the attenuation of signals due to rain is a serious problem for various
necessary communication of systems. Research was done on the attenuation and prediction of rainfall to
build a reliable prediction accuracy of these methods and an assessment they acquire for the applicability
based on the data base are required which can be collected from the meteorological department. In this
research we have collected the rainfall data from Giovani–NASA for the 39 years form 1980-2018.
Many prediction methods for rain attenuation have been discussed, rain covers approximately more
than half of time during a year. Because of rain chances of landslides will be increase high which is very
dangerous for our surroundings. Nonlinear time series was also used in many researches for the rainfall [1]
by using different technique and considering various input as a cause of rain [2]. In India, agriculture is the
primary source of economic growth, accurate rainfall forecasting is critical. Some studies explain the
regression model, neural networks and clustering to improve rainfall prediction [3]. “The approaches based
2. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 11, No. 3, December 2022: 185-193
186
on autoregressive integrated moving average (ARIMA), the fuzzy time series (FST) model, and the non-
parametric method have been discussed in many literatures” [4]. In other studies, a traditional regression
model was adjusted to forecast rainfall by iterating existing data and adding error percentage to the input, as
well as taking numerous inputs of rainfall such as wind-gust, humidity, and temperature.
Modelling of rainfall is a critical component of responsibility in areas like north east India, where
the Indian summer monsoon lasts approximately half the year. There are so many researches and different
techniques for prediction. Some literature of ARIMA models compute the missing observations using the
Kalman filter [5], which allows a partially diffuse initial state vector. Also, spatial autoregressive moving
averages (SARMAS) algorithm calculates an approximation of the multiplicative models [6]. Many
algorithms compute the fast result for ARIMA models [7] also the error estimates for detecting the possible
intervention in the data time series [8]. To calculate the time series data formed by different variations of
monthly data, an improved ARIMA is developed, [9] contemplating the high spatiotemporal variation in
rainfall distribution, developed an ARIMA model for forecasting and prediction of monthly rainfall [10].
Semi-empirical method is also used for the prediction of rain mainly International Telecommunication
Union-Radiocommunication sector (ITU-R) recommended attenuation in slant path link and terrestrial links
which affect the propagation path [11]. Scaling the rain attenuation will benefit the quick monitoring of rain
attenuation by using artificial neural network [12], [13]. To measure the attenuation time series on satellite-
earth link are also done [14]. Evaluation of the forecast accuracy as well as evaluation among the district
fashion suited to a time series model [15] for the modelling. A modified ARIMA modeling technique capture
time correlation and possibility of distribution records [16]. Some architecture is also used to combine simple
tune to ARIMA model [17]. A correction mechanism is run for the sum of the predicted findings in medium
and long-term software programme failure time forecasting [18]. Effectivity of method in literature can also
predict the experiment for the time collection [19], Metro-wheel based ARIMA model shows the stationarity
evaluation and transformation [20] also Box-Jenkins emphases to recognise a fitting time series replica [21]
with some model of forecasting correctness [22] by combining models is dynamic research area for ARIMA
models.
In this paper, we will describe the forecasting of different hill stations with a statistical analysis of
prediction using regression model by taking 39 years of historical data of India [23]. Mostly the tropical areas
are orogrographic in nature, sudden rain in the environment may causes the landslides which is very big
problem for human beings and society and for the agriculture purpose. So, the purpose of this research is to
statistically analyses of rainfall prediction by using historical data so that we prevent the human lives from
the landslide and other natural disaster caused by rain. To mitigate this problem, we have taken the different
tropical region and doing the statistically analyses through the ARIMA model equation and find the different
parameters such as mean, standard deviation and variance after that we also find the function-statistics and
percentile-value. Based on these parameters we got the absolute error which can help us to find the prediction
of rainfall for future use, as for many aspects rainfall prediction is important for human beings to prevent the
risk of landslides and other societal issues.
2. RESEARCH METHOD
We have taken eight regions mention in the Table 1 with twelve different tropical regions, as shown
in Figure 1. For different rainfall seasons all regions we have taken are orographic. This affects the
temperature and hills of that region. So, we collected the data of all these regions mention in the Table 1 from
the Giovani (NASA), to forecasting to be alerted the problems to protect the environment and human lives.
Figure 1. Study area of North east India [24]
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Table 1. Study regions
State Region for study
1 Sikkim Pakyong, Kanchenjunga
2 Meghalaya Cherrapunji, Dawki
3 West-Bengal Darjeling, Ghum
4 Ladakh (Union Territory) Saser-Kangri, Slot Kangri
5 Arunachal Pradesh Itanagar
6 Mizoram Mizoram
7 Tripura Tripura
8 Nagaland Nagaland
Climate of these places are subtropical, a lot of rain seen in months from May-September. The work
flow is shown in Figure 2. The primary cause of rain in these places has the sudden rainfall due to the natural
hazards which threaten human life. So, it is very important to study the area for the betterment of human
beings and to prevent the natural hazards. On the other hand, as comparing with winters, summers include a
lot of rain and a high average temperature. Rainfall is vital to research to prevent landslides and other
difficulties because it is a hilly region with orographic nature.
Figure 2. Work flow of models
Thirty-nine years of historical rainfall data for twelve regions i.e., Cherrapunji, Darjeling, Dawki,
Ghum, Itanagar, Kamchenjunga, Mizoram, Nagaland, Pakyong, Saser Kangri, Slot Kangri, and Tripura are
smoothed and processed with white noise test. After processing, the data is fed into the ARIMA model, which
is fine-tuned for lower prediction error. The model is then calculated in terms of MSE, root mean square error
(RMSE), and mean absolute error (MAE) [5].
3. EQUATION AND METHOD
The ARMA model [1]:
𝐴(𝑧)𝑦(𝑡) = 𝐶(𝑧)𝑒(𝑡) (1)
Equation for Cherrapunji:
A(z) = 1 − 1.001 z−1
(2)
𝐶(𝑧) = 1 − 0.07672 𝑧−1
− 0.4625 𝑧−2
− 0.4216 𝑧−3
Equation for Darjelling:
𝐴(𝑧) = 1 − 1.002 𝑧−1
(3)
𝐶(𝑧) = 1 − 0.1054 𝑧^ − 1 − 0.3352 𝑧^ − 2 − 0.5182 𝑧^ − 3
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4. RESULTS AND DISCUSSION
The developed model is used to forecast monthly precipitation at twelve locations ten steps ahead of
time. The lowest error percentage values of the selected region are further counter-confirmed by forecast
techniques, which suggest that the observed value is closer to forecasting the average rainfall intensity.
Before doing the rainfall prediction, we have done some statistical calculations of these regions, which can
help us to find the betterment of the result. In Table 2, regions are listed with their respective altitudes in
meters. Apart from this, we can get the mean, standard deviation, and variance in this study. In Table 3,
colors are used in the graphs for the prediction of rainfall regions for better understanding.
Table 2. North east stations with varying their altitude
Station Altitude (m) Mean Standard Deviation Variance
Cherrapunji 1,430 215.6086 239.2806 57255.23
Dawki 45 214.6788 238.3682 56819.38
Itanagar 320 189.2824 212.8381 45300.06
Slot Kangri 6,000 54.56021 42.12417 1859.694
Darjelling 2,042 225.4848 253.6668 64346.86
Ghum 2,258 199.7583 224.9138 50586.2
Kanchenjunga 8,500 217.8912 245.6023 60320.5
Mizoram 1,548 293.5116 338.7251 114734.7
Nagaland 1,830 230.2049 260.0278 67614.45
Pakyong 1,120 272.8233 294.8636 86944.56
Saser Kangri 7,500 48.11607 27.18314 738.9232
Tripura 939 203.3454 231.6048 53640.77
Table 3. Colors for the prediction of rainfall for different region
Station Color
Cherrapunji
Darjelling
Dawki
Ghum
Itanagar
Kanchenjunga
Mizoram
Nagaland
Pakyong
Saser Kangri
Slot Kangri
Tripura
Figure 3 depicts the rainfall of three hill stations, Cherrapunji, Darjeling, and Dawki, at altitudes of
1430 m, 2042 m, and 45 m, respectively, over a thirty-nine-year period (1980-2018). As shown in the graph,
rainfall at Cherrapunji is quite high when compared to other hill stations. Cherrapunji is noted for having the
most rainfall in India. As a result, determining expected precipitation for the three stations, is an early
indicator of excessive rainfall.
Figure 3. Prediction for three hill stations: Cherrapunji, Darjeling and Dawki
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Similarly, for the region Ghum, Itanagar, and Kanchenjunga are observed the predicted rainfall in
Figure 4. Here, Kanchenjunga is having the highest altitude which is very rare as compared to the other
regions. This region ranges from 10 °C to 28 °C, the South-West Monsoon brings rain to Kanchenjunga.
Figure 5 shows prediction of rainfall for Mizoram, Nagaland, and Pakyong.
Figure 4. Prediction for Ghum, Itanagar, and Kanchenjunga
Figure 5. Prediction for Ghum, Itanagar, and Kanchenjunga
For Saser Kangri, Slot Kangri, and Tripura, Figure 6 depicts an actual rainfall and predicted rainfall.
Rainfall was correctly predicted by the model that was built over these areas. We have done some error estimation
for all these regions using R2
value, F-statistics, and P-value after doing ten-step ahead prediction for 39 years. F-
statistics, also known as fixation statistics, reflect the level of heterozygosity in a dataset that is statistically
expected. It's calculated theoretically as the ratio of two scaled sums of squares of the dataset's elements. As a
result, it indicates the dataset's variability, while the p-value denotes the level of marginal significance inside a
statistical hypothesis test that represents the occurrence of a specific feature within the data set.
Figure 6. Prediction for Saser Kangri, Slot Kangri, and Tripura
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The observed R-squared is reliable, according to the F-test in Table 4. As a result, the model is
statistically sound and may be used to complex rainfall scenarios such as forecasting. The outcome of the F-
test is further confirmed by the percentile-test (P-test). Table 4 shows that the R-squared is credible and that
the data set utilised was not chosen at random. As a result, the prediction model is statistically sound and may
be used to complex rainfall scenarios such as orographic forecasting. The outcome of the F-test is further
confirmed by the percentile-test (P-test). The residual diagnostics test has been performed before all the
models have been tested, and the best models that produce white noise residuals with well-behaved
autocorrelation function (ACF) plots have been chosen. Table 5 demonstrates that the model coefficients are
less than 10, demonstrating the ease with which complicated variables like orographic rainfall may be
predicted. The RMSE value of the dependent variable, such as historical rainfall, as shown in Table 4, reveals
a close match to the expected estimate. Scale-free measures of fit, such as MAE, are determined, and a few
models are chosen, followed by the best and estimated models based on the lowest RMSE and MAE for
prediction. The scatter index, which is lowest in Cherrapunji, Darjeeling, and Tripura, reveals several
parameters following rainfall forecast delivers the best outcome and because of their orographic nature, MAE
is likewise at a minimum in Cherrapunji, Darjeeling, and Tripura. So, we set the model's na and nc values to
6 and 8 for Darjeeling, but 6 and 4 for Cherrapunji and Tripura, as well as other places, for better results
while na and nc are the model's polynomial order and delays, respectively.
Table 4. Parameters for predicted rainfall of the north east region of India (R2
value, F-statistics, and P-value)
Stations R
2
F- Statics P-Value
Theory Practical Theory Practical
Cherrapunji 8.8699 0.8784 0.8784 0.8667 0.5253
Darjeling 0.9312 0.9148 0.9148 0.9266 0.6742
Dawki 0.9432 0.9028 0.9028 0.9244 0.6601
Ghum 0.8666 0.8916 0.8916 0.8925 0.5503
Itanagar 0.8412 0.8611 0.8611 0.8829 0.5443
Kanchanjunga 0.8752 0.8752 0.8752 0.8805 0.5465
Mizoram 0.7177 0.7322 0.7322 0.7642 0.5100
Nagaland 0.7241 0.7267 0.7267 0.7348 0.5432
Pakyong 0.8790 0.8983 0.8983 0.8760 0.5450
Saser Kangri 0.8780 0.8964 0.8964 0.7811 0.5865
Slot Kangri 0.8044 0.8144 0.8144 0.8948 0.5654
Tripura 0.7047 0.7412 0.7412 0.7787 0.5212
Table 5. Parameters of different regions
Stations SI MAE na nc MSE RMSE
Cherrapunji 0.08812 0.08911 6 4 15.989 22.8465
Darjeling 0.07671 0.07902 6 8 9.585 15.0693
Dawki 0.0797 0.09211 6 4 14.851 19.7044
Ghum 0.7630 0.100702 6 4 9.408 15.0434
Itanagar 0.6890 0.0998 6 4 13.524 16.2211
Kanchenjunga 0.0770 0.0885 6 4 10.693 16.5281
Mizoram 0.8920 0.0899 6 4 15.113 16.993
Nagaland 0.8820 0.0896 6 4 14.991 22.424
Pakyong 0.0847 0.0891 6 4 14.960 21.7080
Saser Kangri 0.0760 0.0888 6 4 15.801 22.73331
Slot Kangri 0.7740 0.0888 6 4 16.800 22.8991
Tripura 0.0724 0.7691 6 4 14.20 21.8895
5. CONCLUSION
Rainfall, which has a direct impact on agriculture, is the main contributor of natural calamities such
as landslides and also various other factors due to rainfall in these regions. As a result, we need to mitigate of
this problem, we must forecast the event at an early stage. The regression model that was optimized this
problem has an acceptable error value for different orographic regions that can be make accurate predictions.
Rainfall data from more stations at higher altitudes will be required in the future to validate the improved rain
forecast model.
ACKNOWLEDGEMENTS
Author thanks to TMA Pai research grant from Sikkim Manipal Institute of Technology, fund
provided by ISRO-RESPOND project also acknowledged to the Goddard Earth Sciences Data and
Information Services Center (GES DISC), National Aeronautics and Space Administration, developed and
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maintains the Giovanni online data system, which was used to create the analysis and visualizations used in
this work (NASA).
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BIOGRAPHIES OF AUTHORS
Pooja Verma received her first degree from CCS University, Computer Sience,
Ghaziabad, Uttar Pradesh, in 2009. She has also master’s degree from Gautam Budha
University, Wireless Communication Networking, Greater Noida, in 2014. She is pursuig
Ph.D. degree from the Sikkim Manipal Institute of Technology, Sikkim Manipal University,
Sikkim, India. She is Currently a research scholar. Her main research interests focus on remote
sensing, artificial intelligence, data modeling and radio propagation, geoscience and frequency
signal. She is getting the TMA Pai research grant from Sikkim Manipal Institute of
Technology. She can be contacted at email: puja20verma@gmail.com.
Dr. Amrita Biswas is presently working as Associate Professor in the department
of Electronics and Communication Engineering and Deputy Registrar (Administration) in
Sikkim Manipal Institute of Technology. She completed her B. Tech in 2004 and M. Tech in
2008 from SMIT. She received her PhD Degree on the topic ‘Development of algorithms for
human face recognition’ in 2016 from SMU. Her area of research includes artificial
intelligence, machine learning, pattern recognition, and data science. She has published papers
on computer vision and image processing in various Journals and Conferences. She has also
guided several projects in the field of machine learning and artificial intelligence. One of the
guided projects- Smart face recognizer using AI won the first prize in Coursera Show Your
Skill Lite, 2020. She can be contacted at email: amrita.a@smit.smu.edu.in.
Dr. Swastika Chakraborty is a professor in the Electronics and Communication
Engineering Department, Narula Institute of Technology, Kolkata, India. Her research
interests include tropospheric radio wave propagation and its associated climatology. She has
authored several research publications and carried out different extramural projects sponsored
by Govt. agencies. She can be contacted at email: swastika1971@gmail.com.