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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Veröffentlicht in:	Water resources management Jg. 24; H. 9; S. 1845 - 1865
Hauptverfasser:	Mohanty, Sheelabhadra, Jha, Madan K, Kumar, Ashwani, Sudheer, K. P
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Dordrecht : Springer Netherlands 01.07.2010 Springer Netherlands Springer Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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