Modelling groundwater levels in an urban coastal aquifer using artificial neural networks

The prediction of groundwater levels in a basin is of immense importance for the management of groundwater resources, especially in coastal regions where the water table fluctuations are to be limited to avoid seawater intrusion. In this paper, an Artificial Neural Network (ANN) methodology is presented to predict groundwater levels in individual wells with one month lead. Groundwater levels were also predicted in neighboring wells using model parameters from the best network of a well. This methodology is applied to an urban coastal aquifer in Andhra Pradesh state, India. The results suggest that the feed forward neural network with Levenberg Marquardt (LM) algorithm is a good choice for predicting groundwater levels in individual wells. Bayesian Regularization (BR) model parameters of Balaji Nagar well are also used successfully to predict groundwater levels in the study area. It was observed that the ANN-based algorithms were a better choice for the prediction of groundwater levels with limited hydrological parameters. Copyright (c) 2007 John Wiley & Sons, Ltd.