R K Panda | IIT Bhubaneswar (original) (raw)
Papers by R K Panda
Agricultural Water Management, 2003
The reported study was undertaken to determine an efficient strategy for management of irrigation... more The reported study was undertaken to determine an efficient strategy for management of irrigation water in case of wheat crop under water stressed conditions in a subtropical sub-humid region. Field experiments were conducted on wheat crop over a period of three years with five different irrigation treatments. Layer-wise soil moisture status was continuously monitored to determine the crop water extraction pattern and thereby the irrigation management depth. Irrigation treatments consisted of different levels of depletion of available soil water. The five levels of depletions considered in the study were 10%, 30%, 45%, 60% and 75%. CERES-wheat growth simulation model was calibrated, validated and used for decision-making. In order to asses the depth and time variation of soil moisture under different scheduling of irrigation, soil moisture was measured periodically in 15-30, 30-45, 45-60, 60-90 and 90-120 cm soil profiles using a neutron probe, while the soil moisture in 0-15 cm soil profile was determined by gravimetric method. It was observed that the plants extracted most of the soil moisture from 0-45 cm soil layer in case of wheat. Therefore, it was recommended that only 0-45 cm of soil profile be considered for scheduling of irrigation in case of wheat crop grown in sandy loam soil in the subtropical regions under water scarcity conditions. The field water use efficiency of wheat crop was found to be the highest when irrigation was scheduled at 45% depletion of ASW. It was therefore recommended that under water scarcity condition, plant extractable soil water depletion of more than 45% of ASW must be avoided even during non-critical stages of the wheat crop grown in medium sandy loam soils in subtropical regions. The calibrated CERES-wheat model was found to be quite efficient in simulation of yield parameters and layer-wise soil moisture extraction pattern. It can therefore be successfully used for decision making in the region.
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.
Hydrological Sciences Journal, 2009
Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side ... more Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side during the rainy season. Due to the nonlinear and fuzzy behaviour of hydrological processes, and in cases of scarcity of relevant data, it is difficult to simulate the desired outflow using physically-based models. Artificial intelligence techniques, namely artificial neural networks (ANN) and an adaptive neurofuzzy inference system (ANFIS), were used in the reported study to estimate the flow at the downstream stretch of a river using flow data for upstream locations. Comparison of the performance of ANN and ANFIS was made by estimating daily outflow from a barrage located in the downstream region of Mahanadi River basin, India, using daily release data from the Hirakud Reservoir, located some distance upstream of the barrage. To obtain the best input-output mapping, five different models with various input combinations were evaluated using both techniques. The significance of the contribution of two upstream tributaries to barrage outflow estimation was also evaluated. Three feed-forward back-propagation training algorithms were used to train the models. Standard performance indices, such as correlation coefficient, index of agreement, root mean square error, modelling efficiency and percentage deviation in peak flow, were used to compare the performance of the models, as well as the training techniques. The results revealed that the neural network with conjugate gradient algorithm performs better than Levenberg-Marquardt and gradient descent algorithms. The model which considers as input the reservoir release up to three antecedent time steps produced the best results. It was found that barrage outflow could be better estimated by the ANFIS than by the ANN technique. Key words neural networks; fuzzy inference system; hydrological processes; training algorithms Réseau de neurones et systèmes d'inférence neuro-floue adaptatif pour la prévision de débit en rivière Résumé Le flux sortant d'un barrage devrait être maintenu à un niveau approprié pour éviter les inondations à l'aval durant la saison humide. En raison du comportement non-linéaire et flou des processus hydrologiques, et dans le cas où les données pertinentes sont rares, il est difficile de simuler le flux sortant souhaitable à l'aide de modèles à bases physiques. Des techniques d'intelligence artificielle, en l'occurrence à base de réseau de neurones artificiels (RNA) et de système d'inférence neuro-floue adaptatif (ANFIS), ont été utilisées dans cette étude pour estimer le débit dans le tronçon aval d'une rivière à l'aide des données de débit en des sites amont. La comparaison des performances des RNA et de ANFIS a été menée pour l'estimation du flux journalier issu d'un barrage situé dans la région aval du basin de la Rivière Mahanadi en Inde, à partir des données de lâchure du Barrage Hirakud situé à l'amont. Afin d'obtenir la meilleure cartographie entrées-sorties, cinq modèles différents avec plusieurs combinaisons d'entrées ont été évalués avec les deux techniques. La significativité de la contribution des deux affluents amont pour l'estimation du flux sortant du barrage a également été évaluée. Trois algorithmes d'apprentissage progressif avec rétropropagation ont été utilisés pour renseigner les modèles. Des indices de performance standard, comme le coefficient de corrélation, l'indice de satisfaction, l'erreur quadratique moyenne, l'efficience de modélisation et le pourcentage d'écart du pic de débit, ont été utilisés pour comparer les performances des modèles ainsi que des techniques d'apprentissage. Les résultats montrent que le réseau de neurones avec un algorithme de gradient conjugué donne de meilleurs résultats que les algorithmes de Levenberg-Marquardt et de gradient descendant. Le modèle qui considère comme entrées les lâchures lors des trois pas de temps antérieurs donne les meilleurs résultats. Le flux sortant du barrage est mieux estimé avec la technique ANFIS qu'avec les RNA. Mots clefs réseaux de neurones; système d'inférence floue; processus hydrologiques; algorithms d'apprentissage Open for discussion until
Agricultural Water Management, 2001
The study was carried out at the experimental farm of the Agricultural and Food Engineering Depar... more The study was carried out at the experimental farm of the Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, India, having a sub-humid climate. A weighing type lysimeter of 75 cm diameter and 75 cm height was installed at the center of a 10 m  10 m experimental plot and daily reference crop evapotranspiration (ET 0) was measured with an electronic datalogger connected to the lysimeter. Grass was used as the reference crop for observing the actual ET. A total of 10 climatological methods were selected for estimating reference crop evapotranspiration on a daily basis. Some of these methods are based on combination theory and others are empirical methods based primarily on solar radiation, temperature and relative humidity. All the methods were originally developed for a well watered reference crop, either alfalfa or grass. An attempt was made in the current study to develop regional relationships between the evapotranspiration measured by the lysimeter and that estimated by the climatological methods, such as Penman, FAO±Penman, FAO±Corrected±Penman, 1982-Kimberley±Penman, Penman± Monteith, Turc±Radiation, Priestley±Taylor, FAO±Radiation, Hargreaves and FAO±Blaney± Criddle. Performance of the climatological methods in estimating the ET 0 values as compared to the lysimeter-measured values was evaluated on the basis of root mean square error (RMSE). Almost all combination methods performed quite well. Radiation methods also gave good results but the ET 0 values estimated by these methods did not match closely with the measured ET 0 values unlike those estimated by the combination methods. The Penman±Monteith equation gave the best result followed by 1982-Kimberly±Penman, FAO±Penman, Turc±Radiation and FAO±Blaney± Criddle. The RMSE in all the cases varied between 0.08 and 0.756. Crop-coef®cients (K c) were estimated for potato crop at different stages of growth, at the same location, based on lysimeter measured actual ET and the reference crop evapotranspiration estimated by various methods. The measured values of crop-coef®cient for potato crop at four stages of growth, such as initial, crop development, reproductive and maturity were 0.42, 0.85, 1.27 Agricultural Water Management 50 (2001) 9±25
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.
Agricultural Water Management, 2003
The reported study was undertaken to determine an efficient strategy for management of irrigation... more The reported study was undertaken to determine an efficient strategy for management of irrigation water in case of wheat crop under water stressed conditions in a subtropical sub-humid region. Field experiments were conducted on wheat crop over a period of three years with five different irrigation treatments. Layer-wise soil moisture status was continuously monitored to determine the crop water extraction pattern and thereby the irrigation management depth. Irrigation treatments consisted of different levels of depletion of available soil water. The five levels of depletions considered in the study were 10%, 30%, 45%, 60% and 75%. CERES-wheat growth simulation model was calibrated, validated and used for decision-making. In order to asses the depth and time variation of soil moisture under different scheduling of irrigation, soil moisture was measured periodically in 15-30, 30-45, 45-60, 60-90 and 90-120 cm soil profiles using a neutron probe, while the soil moisture in 0-15 cm soil profile was determined by gravimetric method. It was observed that the plants extracted most of the soil moisture from 0-45 cm soil layer in case of wheat. Therefore, it was recommended that only 0-45 cm of soil profile be considered for scheduling of irrigation in case of wheat crop grown in sandy loam soil in the subtropical regions under water scarcity conditions. The field water use efficiency of wheat crop was found to be the highest when irrigation was scheduled at 45% depletion of ASW. It was therefore recommended that under water scarcity condition, plant extractable soil water depletion of more than 45% of ASW must be avoided even during non-critical stages of the wheat crop grown in medium sandy loam soils in subtropical regions. The calibrated CERES-wheat model was found to be quite efficient in simulation of yield parameters and layer-wise soil moisture extraction pattern. It can therefore be successfully used for decision making in the region.
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.
Hydrological Sciences Journal, 2009
Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side ... more Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side during the rainy season. Due to the nonlinear and fuzzy behaviour of hydrological processes, and in cases of scarcity of relevant data, it is difficult to simulate the desired outflow using physically-based models. Artificial intelligence techniques, namely artificial neural networks (ANN) and an adaptive neurofuzzy inference system (ANFIS), were used in the reported study to estimate the flow at the downstream stretch of a river using flow data for upstream locations. Comparison of the performance of ANN and ANFIS was made by estimating daily outflow from a barrage located in the downstream region of Mahanadi River basin, India, using daily release data from the Hirakud Reservoir, located some distance upstream of the barrage. To obtain the best input-output mapping, five different models with various input combinations were evaluated using both techniques. The significance of the contribution of two upstream tributaries to barrage outflow estimation was also evaluated. Three feed-forward back-propagation training algorithms were used to train the models. Standard performance indices, such as correlation coefficient, index of agreement, root mean square error, modelling efficiency and percentage deviation in peak flow, were used to compare the performance of the models, as well as the training techniques. The results revealed that the neural network with conjugate gradient algorithm performs better than Levenberg-Marquardt and gradient descent algorithms. The model which considers as input the reservoir release up to three antecedent time steps produced the best results. It was found that barrage outflow could be better estimated by the ANFIS than by the ANN technique. Key words neural networks; fuzzy inference system; hydrological processes; training algorithms Réseau de neurones et systèmes d'inférence neuro-floue adaptatif pour la prévision de débit en rivière Résumé Le flux sortant d'un barrage devrait être maintenu à un niveau approprié pour éviter les inondations à l'aval durant la saison humide. En raison du comportement non-linéaire et flou des processus hydrologiques, et dans le cas où les données pertinentes sont rares, il est difficile de simuler le flux sortant souhaitable à l'aide de modèles à bases physiques. Des techniques d'intelligence artificielle, en l'occurrence à base de réseau de neurones artificiels (RNA) et de système d'inférence neuro-floue adaptatif (ANFIS), ont été utilisées dans cette étude pour estimer le débit dans le tronçon aval d'une rivière à l'aide des données de débit en des sites amont. La comparaison des performances des RNA et de ANFIS a été menée pour l'estimation du flux journalier issu d'un barrage situé dans la région aval du basin de la Rivière Mahanadi en Inde, à partir des données de lâchure du Barrage Hirakud situé à l'amont. Afin d'obtenir la meilleure cartographie entrées-sorties, cinq modèles différents avec plusieurs combinaisons d'entrées ont été évalués avec les deux techniques. La significativité de la contribution des deux affluents amont pour l'estimation du flux sortant du barrage a également été évaluée. Trois algorithmes d'apprentissage progressif avec rétropropagation ont été utilisés pour renseigner les modèles. Des indices de performance standard, comme le coefficient de corrélation, l'indice de satisfaction, l'erreur quadratique moyenne, l'efficience de modélisation et le pourcentage d'écart du pic de débit, ont été utilisés pour comparer les performances des modèles ainsi que des techniques d'apprentissage. Les résultats montrent que le réseau de neurones avec un algorithme de gradient conjugué donne de meilleurs résultats que les algorithmes de Levenberg-Marquardt et de gradient descendant. Le modèle qui considère comme entrées les lâchures lors des trois pas de temps antérieurs donne les meilleurs résultats. Le flux sortant du barrage est mieux estimé avec la technique ANFIS qu'avec les RNA. Mots clefs réseaux de neurones; système d'inférence floue; processus hydrologiques; algorithms d'apprentissage Open for discussion until
Agricultural Water Management, 2001
The study was carried out at the experimental farm of the Agricultural and Food Engineering Depar... more The study was carried out at the experimental farm of the Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, India, having a sub-humid climate. A weighing type lysimeter of 75 cm diameter and 75 cm height was installed at the center of a 10 m  10 m experimental plot and daily reference crop evapotranspiration (ET 0) was measured with an electronic datalogger connected to the lysimeter. Grass was used as the reference crop for observing the actual ET. A total of 10 climatological methods were selected for estimating reference crop evapotranspiration on a daily basis. Some of these methods are based on combination theory and others are empirical methods based primarily on solar radiation, temperature and relative humidity. All the methods were originally developed for a well watered reference crop, either alfalfa or grass. An attempt was made in the current study to develop regional relationships between the evapotranspiration measured by the lysimeter and that estimated by the climatological methods, such as Penman, FAO±Penman, FAO±Corrected±Penman, 1982-Kimberley±Penman, Penman± Monteith, Turc±Radiation, Priestley±Taylor, FAO±Radiation, Hargreaves and FAO±Blaney± Criddle. Performance of the climatological methods in estimating the ET 0 values as compared to the lysimeter-measured values was evaluated on the basis of root mean square error (RMSE). Almost all combination methods performed quite well. Radiation methods also gave good results but the ET 0 values estimated by these methods did not match closely with the measured ET 0 values unlike those estimated by the combination methods. The Penman±Monteith equation gave the best result followed by 1982-Kimberly±Penman, FAO±Penman, Turc±Radiation and FAO±Blaney± Criddle. The RMSE in all the cases varied between 0.08 and 0.756. Crop-coef®cients (K c) were estimated for potato crop at different stages of growth, at the same location, based on lysimeter measured actual ET and the reference crop evapotranspiration estimated by various methods. The measured values of crop-coef®cient for potato crop at four stages of growth, such as initial, crop development, reproductive and maturity were 0.42, 0.85, 1.27 Agricultural Water Management 50 (2001) 9±25
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.