Abstract
Accurate forecasting of groundwater level is important for sustainable utilization and management of groundwater resources. The performances of Feed Forward Neural Network (FNN), Radial Basis Function Neural Network (RBF) and Elman or Fully Recurrent Neural Network (RNN) Artificial Neural Networks (ANN) in groundwater level forecasting were examined in order to identify an optimal ANN model for groundwater level forecast. Bayesian Regularization (BR), Levenberg-Marquardt (LM) and Gradient Descent with Momentum and Adaptive Learning Rate Back Propagation (GDX) training algorithms were used to train each ANN. Devasugurnala watershed located at northern part of Raichur district, Karnataka, under middle Krishna river basin was selected for the study. The results revealed that FFN-LM model with 3-10-1 architecture with least RMSE and highest correlation coefficient values was most efficient for monthly groundwater level forecasting for the study area, and was a promising tool for the forecasting of groundwater level.
Published Version
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