GURJEET SINGH | IIT Bhubaneswar (original) (raw)

Papers by GURJEET SINGH

The major objective of this study was to analyze the trend and variability of rainfall in the mid... more The major objective of this study was to analyze the trend and variability of rainfall in the middle Mahandi river basin located in eastern India. The trend of variation of extreme rainfall events has predominant effect on agricultural water management and extreme hydrological events such as floods and droughts. Mahanadi river basin is one of the major river basins of India having an area of 1,41,589 km² and divided into three regions: Upper, middle and delta region. The middle region of Mahanadi river basin has an area of 48,700 km² and it is mostly dominated by agricultural land, where agriculture is mostly rainfed. The study region has five Agro-climatic zones namely: East and South Eastern Coastal Plain, North Eastern Ghat, Western Undulating Zone, Western Central Table Land and Mid Central Table Land, which were numbered as zones 1 to 5 respectively for convenience in reporting. In the present study, analysis of variability and tren...

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate ... more Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA's Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km 2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, subtropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

Accurate estimation of sediment yield from watershed and its sub-watersheds is a prerequisite for... more Accurate estimation of sediment yield from watershed and its
sub-watersheds is a prerequisite for effective watershed management. Reported study was undertaken in a small agricultural watershed namely Kapgari in Eastern India for estimation of daily sediment yield. On the basis of drainage
network and land topography, the watershed was subdivided into three sub-watersheds. Bootstrap technique was used to develop unbiased artificial neural network (ANN) models to estimate the daily sediment yield with limited quantum of continuously monitored sediment yield data from the watershed. Bootstrap-based artificial neural network (BANN) were developed using only major weather variables such as rainfall and temperature for estimation of daily sediment yield. Results illustrate that the highest coefficient of simulation efficiency values of 0.887, 0.869, 0.904 and 0.898 for estimation of daily sediment yield from watershed and its sub-watersheds were observed by addition of one day lag rainfall and present day maximum and minimum temperature with present day rainfall.

Journal of Hydroinformatics, Jul 4, 2014

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern I... more The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was also investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

International Journal of Earth Sciences and Engineering , Oct 2011

Land and water are the two most vital natural resources of the world and these resources must be ... more Land and water are the two most vital natural resources of the world and these resources must be conserved and maintained carefully for environmental protection and ecological balance. Estimation of sediment yield is prerequisite for conservation and management of water resources and for many hydrological applications. Therefore, this study was undertaken for a small agricultural watershed, Kapgari having a drainage area of 973 ha for prediction of daily sediment yield. Artificial neural network (ANN) models were developed, to predict sediment yield on a daily basis for the watershed. A total of four ANN models were developed using rainfall and temperature data for predicting
sediment yield for Kapgari watershed and its three subwatersheds. Due to smaller data set availability a10fold cross validation method was used for selecting the best performing models and to avoid neural network overfitting during the training process. As a result models considering both rainfall and temperature as inputs performed better than those considering rainfall alone as input. Training and testing results of selected models show that models were predicting the daily sediment yield satisfactorily. Therefore these models can be used for estimating the sediment yield on a daily basis for Kapgari watershed and its subwatersheds.

International Journal of Applied Engineering Research , Nov 2011

Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Srikakulam... more Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Srikakulam is an underdeveloped district of Andhrapradesh, India where ground water is the only resource for catering the drinking, industrial and agricultural needs. Scarcity of potable water is one of the serious problems to the people living in this area. In the present work an attempt is made to develop spatial distribution map of the water quality and to formulate multiple regression equations for assessment of ground water quality of Srikakulam. Data used were related to 3612 sampling stations spread over the 38 mandals of the Srikakulam District. The data on spatial distribution is very much useful for protection and management of ground water quality. Geostatistics methods are one of the most advanced techniques for interpolation of groundwater quality. In this research, Kriging method is used for predicting spatial distribution of groundwater quality index. Map showing the spatial distribution of ground water quality index is developed using GIS. The multiple linear regression method is used to quantify the relationship between several independent or predictor variables and a dependant variable. Correlation studies are very much useful to predict the parameter of interest or the missing values in the data sets. Here 90% of the data is used for model development and 10% data is used for model testing. Regression analysis revealed high degree of correlation (r 0.9) between various parameters and 2720 K. Sundara Kumar et al modeled equations indicated high degree of linearity among independent and dependent variables.

The major objective of this study was to analyze the trend and variability of rainfall in the mid... more The major objective of this study was to analyze the trend and variability of rainfall in the middle Mahandi river basin located in eastern India. The trend of variation of extreme rainfall events has predominant effect on agricultural water management and extreme hydrological events such as floods and droughts. Mahanadi river basin is one of the major river basins of India having an area of 1,41,589 km² and divided into three regions: Upper, middle and delta region. The middle region of Mahanadi river basin has an area of 48,700 km² and it is mostly dominated by agricultural land, where agriculture is mostly rainfed. The study region has five Agro-climatic zones namely: East and South Eastern Coastal Plain, North Eastern Ghat, Western Undulating Zone, Western Central Table Land and Mid Central Table Land, which were numbered as zones 1 to 5 respectively for convenience in reporting. In the present study, analysis of variability and tren...

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate ... more Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA's Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km 2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, subtropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

Accurate estimation of sediment yield from watershed and its sub-watersheds is a prerequisite for... more Accurate estimation of sediment yield from watershed and its
sub-watersheds is a prerequisite for effective watershed management. Reported study was undertaken in a small agricultural watershed namely Kapgari in Eastern India for estimation of daily sediment yield. On the basis of drainage
network and land topography, the watershed was subdivided into three sub-watersheds. Bootstrap technique was used to develop unbiased artificial neural network (ANN) models to estimate the daily sediment yield with limited quantum of continuously monitored sediment yield data from the watershed. Bootstrap-based artificial neural network (BANN) were developed using only major weather variables such as rainfall and temperature for estimation of daily sediment yield. Results illustrate that the highest coefficient of simulation efficiency values of 0.887, 0.869, 0.904 and 0.898 for estimation of daily sediment yield from watershed and its sub-watersheds were observed by addition of one day lag rainfall and present day maximum and minimum temperature with present day rainfall.

Journal of Hydroinformatics, Jul 4, 2014

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern I... more The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was also investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

International Journal of Earth Sciences and Engineering , Oct 2011

Land and water are the two most vital natural resources of the world and these resources must be ... more Land and water are the two most vital natural resources of the world and these resources must be conserved and maintained carefully for environmental protection and ecological balance. Estimation of sediment yield is prerequisite for conservation and management of water resources and for many hydrological applications. Therefore, this study was undertaken for a small agricultural watershed, Kapgari having a drainage area of 973 ha for prediction of daily sediment yield. Artificial neural network (ANN) models were developed, to predict sediment yield on a daily basis for the watershed. A total of four ANN models were developed using rainfall and temperature data for predicting
sediment yield for Kapgari watershed and its three subwatersheds. Due to smaller data set availability a10fold cross validation method was used for selecting the best performing models and to avoid neural network overfitting during the training process. As a result models considering both rainfall and temperature as inputs performed better than those considering rainfall alone as input. Training and testing results of selected models show that models were predicting the daily sediment yield satisfactorily. Therefore these models can be used for estimating the sediment yield on a daily basis for Kapgari watershed and its subwatersheds.

International Journal of Applied Engineering Research , Nov 2011

Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Srikakulam... more Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Srikakulam is an underdeveloped district of Andhrapradesh, India where ground water is the only resource for catering the drinking, industrial and agricultural needs. Scarcity of potable water is one of the serious problems to the people living in this area. In the present work an attempt is made to develop spatial distribution map of the water quality and to formulate multiple regression equations for assessment of ground water quality of Srikakulam. Data used were related to 3612 sampling stations spread over the 38 mandals of the Srikakulam District. The data on spatial distribution is very much useful for protection and management of ground water quality. Geostatistics methods are one of the most advanced techniques for interpolation of groundwater quality. In this research, Kriging method is used for predicting spatial distribution of groundwater quality index. Map showing the spatial distribution of ground water quality index is developed using GIS. The multiple linear regression method is used to quantify the relationship between several independent or predictor variables and a dependant variable. Correlation studies are very much useful to predict the parameter of interest or the missing values in the data sets. Here 90% of the data is used for model development and 10% data is used for model testing. Regression analysis revealed high degree of correlation (r 0.9) between various parameters and 2720 K. Sundara Kumar et al modeled equations indicated high degree of linearity among independent and dependent variables.