Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern...

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Vydané v:	Water resources management Ročník 24; číslo 9; s. 1845 - 1865
Hlavní autori:	Mohanty, Sheelabhadra, Jha, Madan K, Kumar, Ashwani, Sudheer, K. P
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Dordrecht : Springer Netherlands 01.07.2010 Springer Netherlands Springer Springer Nature B.V
Predmet:	Algorithms Aquifers Artificial neural network Atmospheric Sciences Back propagation Backpropagation GDX algorithm basins Bayesian analysis Bayesian regularization algorithm Civil Engineering Clusters Developing countries drainage water Earth and Environmental Science Earth Sciences Earth, ocean, space Environment Evaporation Exact sciences and technology Forecasting Freshwater Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater level prediction Groundwater levels Hydrogeology hydrologic models Hydrology Hydrology. Hydrogeology Hydrology/Water Resources India Irrigation islands Lavenberg-Marquardt algorithm LDCs learning Learning theory Lithology Mathematical models momentum Neural networks Pan evaporation Parameter estimation planning prediction rain River island Rivers Studies Surface water Surface-groundwater relations Training tropics Water resources water table Asia India Backpropagation GDX algorithm Groundwater level prediction Artificial neural network Bayesian regularization algorithm River island Lavenberg-Marquardt algorithm rivers algorithms models rainfall Bayesian regularization algorithm neural networks ground water water table accuracy lead pumping surface water planning water resources aquifers islands water resource management prediction evaporation
ISSN:	0920-4741, 1573-1650
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Abstract	Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.
AbstractList	Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well. Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz, gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well. [PUBLICATION ABSTRACT] Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well. Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.
Author	Mohanty, Sheelabhadra Jha, Madan K Kumar, Ashwani Sudheer, K. P
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Issue	9
Keywords	Backpropagation GDX algorithm Groundwater level prediction Artificial neural network Bayesian regularization algorithm River island Lavenberg-Marquardt algorithm rivers algorithms models rainfall Bayesian regularization algorithm neural networks ground water water table accuracy lead pumping surface water planning water resources aquifers islands water resource management prediction evaporation
Language	English
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PublicationSubtitle	An International Journal - Published for the European Water Resources Association (EWRA)
PublicationTitle	Water resources management
PublicationTitleAbbrev	Water Resour Manage
PublicationYear	2010
Publisher	Dordrecht : Springer Netherlands Springer Netherlands Springer Springer Nature B.V
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Snippet	Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present...
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SubjectTerms	Algorithms Aquifers Artificial neural network Atmospheric Sciences Back propagation Backpropagation GDX algorithm basins Bayesian analysis Bayesian regularization algorithm Civil Engineering Clusters Developing countries drainage water Earth and Environmental Science Earth Sciences Earth, ocean, space Environment Evaporation Exact sciences and technology Forecasting Freshwater Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater level prediction Groundwater levels Hydrogeology hydrologic models Hydrology Hydrology. Hydrogeology Hydrology/Water Resources India Irrigation islands Lavenberg-Marquardt algorithm LDCs learning Learning theory Lithology Mathematical models momentum Neural networks Pan evaporation Parameter estimation planning prediction rain River island Rivers Studies Surface water Surface-groundwater relations Training tropics Water resources water table
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Title	Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India
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