Papers by Pravendra Kumar
Scientific Reports, 2021
Rivers carry suspended sediments along with their flow. These sediments deposit at different plac... more Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory m...
MAUSAM
The rainfall intensity-duration-frequency (IDF) relationship plays an important toolin planning,d... more The rainfall intensity-duration-frequency (IDF) relationship plays an important toolin planning,designing, evaluatingand operatingof water resource projects, water resources development and management.It is necessary to examine the location-specific relationship between rainfall components, intensity, duration and frequency due to their spatiotemporal variation. In this article, weinvestigate the relationship between the rainfall intensity and its components and develop nomographs for Washim, Chandrapur and Yeotmal districts in Maharashtra, India. We also studied the rainfall charts of various stations for maximum annual rainfall intensities of selected duration. The frequency lines are computed for the above locations.. An empirical studyisconducted to determine the value of constants ‘a’ and ‘b’ and that of ‘K’ (Nemec, 1973).The nomographs are also developed for the rainfall intensity-duration-frequency (IDF) relationships (Luzzadar, 1964). Adequacy of the results istested by stat...
Journal of Modeling and Simulation of Materials
The main objective of this study was to evaluate and compare the performances of rainfall-runoff ... more The main objective of this study was to evaluate and compare the performances of rainfall-runoff models that were developed by using support vector machines (SVMs). Rainfall and runoff data of Haripura and Baur dams were adopted on daily basis from Irrigation Division Rudrapur in Uttarakhand. In this study, radial kernel function was used. As the values of Cost function (C), and varies, performances of the models can be altered. So, at optimum values of these variables, there exists a best correlation between rainfall and runoff. It can be inferred from the study that SVM models provide satisfactory results for both dams. These results can be used for runoff prediction for various purpose such as irrigation etc.
Indian Journal of Ecology, 2018
International Journal of Chemical Studies, 2017
Accurate estimation of suspended sediment load carried by rivers is of utmost importance in the s... more Accurate estimation of suspended sediment load carried by rivers is of utmost importance in the soil and water conservation practices in the watershed and also in large number of hydro-environmental issues such as planning, design and operations of reservoirs, dams and environmental impact assessment. This study explores the abilities of statistical models to improve the accuracy of rainfall-streamflow-suspended sediment relationships in daily suspended sediment estimation. In this study, a comparison was made between multiple linear regression and artificial neural networks (ANNs) for the Vamsadhara river catchment. Daily rainfall-runoff and suspended sediment data were used as inputs and outputs. The performance results based on three different types of indicators viz. root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) revealed that ANN (RMSE-110.15 kg/sec, r-0.97 and CE value 94.22 % ) can predict sediment load more efficiently than trad...
Sustainability
Reference evapotranspiration (ETo) plays an important role in agriculture applications such as ir... more Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evalu...
Applied Water Science
Many real water issues involve rivers’ sediment load or the load that rivers can bring without de... more Many real water issues involve rivers’ sediment load or the load that rivers can bring without degrading the fluvial ecosystem. Therefore, the assessment of sediments carried by a river is also crucial in the planning and designing of various water resource projects. In the current study, five different data-driven techniques, namely artificial neural network (ANN), wavelet-based artificial neural network (WANN), support vector machine (SVM), wavelet-based support vector machine (WSVM), and multiple-linear regression (MLR) techniques, were employed for time-series modeling of daily suspended sediment concentration (SSC). Hydrological datasets containing the daily stage (h), discharge (Q), and SSC for 10 years (2004–2013) from June to October at Adityapur and Ghatshila station of Subernrekha river basin, Jharkhand, India, were considered for analysis. The Gamma test was used to determine the input variables in the first step. Various combinations were made by lagging the maximum thre...
Arabian Journal of Geosciences, 2022
Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansi... more Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansion in activities such as urbanization, socio-economic activities, and environmental changes. Hence, detecting the effects of spatio-temporal variability on rainfall-runoff transformation is crucial. In this study, the conventional Natural Resources Conservation Service Curve Number (NRCS-CN) method was modified using slope factor as the conventional NRCS-CN method does not consider slope. The maximum change in spatial extent was observed in the case of bare soil followed by built-up land, agricultural land, dense forest, open forest, and water body over the time interval of 13 years (1999 to 2011). The area under low vegetative coverage (LVC) was observed to be increased from 697.68 to 999.30 km2 over 13 years. The areas under medium vegetative coverage (MVC) and high vegetative coverage (HVC) were observed to be decreased from 1081.93 to 914.54 km2 and 137.56 to 3.33 km2, respectively over 13 years. These changes in the spatial extent of LVC, MVC, and HVC were found to be responsible to increase the curve number (CN) of the study area. Over the time interval of 13 years, the slope-corrected weighted curve number (CNwα) values under dry (AMC-I), normal (AMC-II), and wet (AMC-III) conditions were observed to be increased from 66.55 to 69.54, 80.65 to 82.34, and 90.23 to 91.05, respectively. It was observed that the average runoff coefficient has been increased from 0.41 to 0.43 over 13-year interval which is responsible to increase the runoff. The coefficient of determination (R2) between estimated and observed runoff was found to be about 0.77 which indicated the good predictive performance of the modified NRCS-CN method. This study is helpful for detecting the temporal land use/land cover change and its effects on runoff generation. This study provides importance of construction of rainwater harvesting structures for the purpose of groundwater recharge.
Environmental Science and Pollution Research, 2021
The information about different morphometric parameters of any watershed is necessary for better ... more The information about different morphometric parameters of any watershed is necessary for better watershed management and planning. This study aimed to investigate morphometric characteristics, to assess the soil erosion risk, and to prioritize different sub-watersheds of the Koyna River basin, India, with two different approaches using geospatial technology. Different linear, shape, and relief parameters of the basin were estimated and analyzed. The linear and shape parameters indicated that the basin has less flood hazard. The relief parameters indicated that the basin has moderate roughness and unevenness. The parallel drainage pattern is dominant inside the basin due to the highly elongated nature of the basin. The bifurcation ratio ( R b ) indicated lithological and geological variations inside the basin. Two different approaches namely morphometric analysis and empirical Revised Universal Soil Loss Equation (RUSLE) method were applied for prioritization of different sub-watersheds. Rainfall, soil, digital elevation model (DEM), and normalized difference vegetation index (NDVI) data were used for identifying erosion-prone zones with RUSLE analysis. Based on RUSLE analysis, the entire study area was divided into five soil erosion risk classes namely very slight (80.43 %), slight (14.94 %), moderate (3.21 %), severe (0.79 %), and very severe (0.63%), respectively. Most of the study area was found to be under a very slight soil erosion vulnerability class based on the RUSLE approach. The conservation practices should be carried out as per the priority ranking of different sub-watershed based on soil erosion rates. The results found in this study can surely assist in the implementation of soil conservation planning and management practices to reduce soil loss in the Koyna River basin of India.
Arabian Journal of Geosciences, 2022
Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansi... more Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansion in activities such as urbanization, socio-economic activities, and environmental changes. Hence, detecting the effects of spatio-temporal variability on rainfall-runoff transformation is crucial. In this study, the conventional Natural Resources Conservation Service Curve Number (NRCS-CN) method was modified using slope factor as the conventional NRCS-CN method does not consider slope. The maximum change in spatial extent was observed in the case of bare soil followed by built-up land, agricultural land, dense forest, open forest, and water body over the time interval of 13 years (1999 to 2011). The area under low vegetative coverage (LVC) was observed to be increased from 697.68 to 999.30 km2 over 13 years. The areas under medium vegetative coverage (MVC) and high vegetative coverage (HVC) were observed to be decreased from 1081.93 to 914.54 km2 and 137.56 to 3.33 km2, respectively over 13 years. These changes in the spatial extent of LVC, MVC, and HVC were found to be responsible to increase the curve number (CN) of the study area. Over the time interval of 13 years, the slope-corrected weighted curve number (CNwα) values under dry (AMC-I), normal (AMC-II), and wet (AMC-III) conditions were observed to be increased from 66.55 to 69.54, 80.65 to 82.34, and 90.23 to 91.05, respectively. It was observed that the average runoff coefficient has been increased from 0.41 to 0.43 over 13-year interval which is responsible to increase the runoff. The coefficient of determination (R2) between estimated and observed runoff was found to be about 0.77 which indicated the good predictive performance of the modified NRCS-CN method. This study is helpful for detecting the temporal land use/land cover change and its effects on runoff generation. This study provides importance of construction of rainwater harvesting structures for the purpose of groundwater recharge.
Arabian Journal of Geosciences, 2022
Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansi... more Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansion in activities such as urbanization, socio-economic activities, and environmental changes. Hence, detecting the effects of spatio-temporal variability on rainfall-runoff transformation is crucial. In this study, the conventional Natural Resources Conservation Service Curve Number (NRCS-CN) method was modified using slope factor as the conventional NRCS-CN method does not consider slope. The maximum change in spatial extent was observed in the case of bare soil followed by built-up land, agricultural land, dense forest, open forest, and water body over the time interval of 13 years (1999 to 2011). The area under low vegetative coverage (LVC) was observed to be increased from 697.68 to 999.30 km2 over 13 years. The areas under medium vegetative coverage (MVC) and high vegetative coverage (HVC) were observed to be decreased from 1081.93 to 914.54 km2 and 137.56 to 3.33 km2, respectively over 13 years. These changes in the spatial extent of LVC, MVC, and HVC were found to be responsible to increase the curve number (CN) of the study area. Over the time interval of 13 years, the slope-corrected weighted curve number (CNwα) values under dry (AMC-I), normal (AMC-II), and wet (AMC-III) conditions were observed to be increased from 66.55 to 69.54, 80.65 to 82.34, and 90.23 to 91.05, respectively. It was observed that the average runoff coefficient has been increased from 0.41 to 0.43 over 13-year interval which is responsible to increase the runoff. The coefficient of determination (R2) between estimated and observed runoff was found to be about 0.77 which indicated the good predictive performance of the modified NRCS-CN method. This study is helpful for detecting the temporal land use/land cover change and its effects on runoff generation. This study provides importance of construction of rainwater harvesting structures for the purpose of groundwater recharge.
Environmental Science and Pollution Research, 2021
The information about different morphometric parameters of any watershed is necessary for better ... more The information about different morphometric parameters of any watershed is necessary for better watershed management and planning. This study aimed to investigate morphometric characteristics, to assess the soil erosion risk, and to prioritize different sub-watersheds of the Koyna River basin, India, with two different approaches using geospatial technology. Different linear, shape, and relief parameters of the basin were estimated and analyzed. The linear and shape parameters indicated that the basin has less flood hazard. The relief parameters indicated that the basin has moderate roughness and unevenness. The parallel drainage pattern is dominant inside the basin due to the highly elongated nature of the basin. The bifurcation ratio ( R b ) indicated lithological and geological variations inside the basin. Two different approaches namely morphometric analysis and empirical Revised Universal Soil Loss Equation (RUSLE) method were applied for prioritization of different sub-watersheds. Rainfall, soil, digital elevation model (DEM), and normalized difference vegetation index (NDVI) data were used for identifying erosion-prone zones with RUSLE analysis. Based on RUSLE analysis, the entire study area was divided into five soil erosion risk classes namely very slight (80.43 %), slight (14.94 %), moderate (3.21 %), severe (0.79 %), and very severe (0.63%), respectively. Most of the study area was found to be under a very slight soil erosion vulnerability class based on the RUSLE approach. The conservation practices should be carried out as per the priority ranking of different sub-watershed based on soil erosion rates. The results found in this study can surely assist in the implementation of soil conservation planning and management practices to reduce soil loss in the Koyna River basin of India.
Journal of Soil and Water Conservation, 2021
Journal of Soil and Water Conservation, 2021
Accurate rainfall forecasting is very necessary for water resource management. Recently, several ... more Accurate rainfall forecasting is very necessary for water resource management. Recently, several modeling approaches have been investigated to perform such forecasting task. In the present study, possibility of forecasting rainfall in Junagadh has been analyzed through feed forward artificial neural network models. The 30 years data has been used for training and testing the ANN networks. In formulating the ANN based Predictive model, single and double hidden layers network have been constructed. The performance of models have been evaluated using Correlation coefficient, Mean Square Error, Normalized Mean Square Error, Akaike’s information criterion, Coefficient of Efficiency and volumetric error, and two best suitable models (7-12-13-1 and 4-6-4-1) have been selected from case I and II for rainfall forecasting in Junagadh. Based on the performance evaluation of the models, two models found suitable for prediction of daily rainfall for the study area.
Journal of Soil and Water Conservation, 2016
The present study was undertaken to develop the rainfall erosivity models for Dehradun district, ... more The present study was undertaken to develop the rainfall erosivity models for Dehradun district, Uttarakhand, India for estimating the erosivity index values and for establishing the most effective relationship between erosivity and daily rainfall values for the study area. For this, two models namely linear and exponential relationship between erosivity and daily rainfall values were developed and results were compared with the model proposed by Central Soil and Water Conservation Research and Training Institute, Dehradun for their suitability in the region. Model performance was evaluated using two statistical indices such as absolute prediction error and coefficient of efficiency. Absolute prediction error was found to be 13.09% and 19.58% for linear and exponential relationship respectively whereas coefficient of efficiency was found to be 99.24% and 98.54% for linear and exponential relationship respectively. APE of 55.10% and CE of 45.37% were found for the model proposed by C...
Accurate rainfall forecasting is very necessary for water resource management. Recently, several ... more Accurate rainfall forecasting is very necessary for water resource management. Recently, several modeling approaches have been investigated to perform such forecasting task. In the present study, possibility of forecasting rainfall in Junagadh has been analyzed through feed forward artificial neural network models. The 30 years data has been used for training and testing the ANN networks. In formulating the ANN based Predictive model, single and double hidden layers network have been constructed. The performance of models have been evaluated using Correlation coefficient, Mean Square Error, Normalized Mean Square Error, Akaike’s information criterion, Coefficient of Efficiency and volumetric error, and two best suitable models (7-12-13-1 and 4-6-4-1) have been selected from case I and II for rainfall forecasting in Junagadh. Based on the performance evaluation of the models, two models found suitable for prediction of daily rainfall for the study area.
Journal of Soil and Water Conservation, 2016
The present study was undertaken to develop the rainfall erosivity models for Dehradun district, ... more The present study was undertaken to develop the rainfall erosivity models for Dehradun district, Uttarakhand, India for estimating the erosivity index values and for establishing the most effective relationship between erosivity and daily rainfall values for the study area. For this, two models namely linear and exponential relationship between erosivity and daily rainfall values were developed and results were compared with the model proposed by Central Soil and Water Conservation Research and Training Institute, Dehradun for their suitability in the region. Model performance was evaluated using two statistical indices such as absolute prediction error and coefficient of efficiency. Absolute prediction error was found to be 13.09% and 19.58% for linear and exponential relationship respectively whereas coefficient of efficiency was found to be 99.24% and 98.54% for linear and exponential relationship respectively. APE of 55.10% and CE of 45.37% were found for the model proposed by C...
The present study was undertaken to estimate the suspended sediment load from the Vamsadhara rive... more The present study was undertaken to estimate the suspended sediment load from the Vamsadhara river basin comprising of 7820 km area, situated between Mahanadi and Godavari river basins. Three daily input data groups or cases were employed using Artificial Neural Network (ANN), Adaptive NeuroFuzzy Inference System (ANFIS), Fuzzy Logic (FL), Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) to find the effect of different inputs on the suspended sediment load. Input 1 consists of Pt, Qt, Qt−1, St−1 as inputs to the model to predict St. Input 2 consists of Pt-1, Qt, Qt-1, St-1 and Input 3 consist of Pt-1, Qt, Qt-2, St-1. The developed models were trained and tested. Three statistical parameters: root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) were used to compare the results of the models. Based on the performance analysis results revealed that the ANFIS model (RMSE-44.02 kg/sec, r-0.995 and CE-99.06%) outperformed other soft...
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Papers by Pravendra Kumar