Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks

Abstract

Groundwater is a prominent source of drinking and domestic water in the world. In this context a reliable water supply policy, specifically during the dry season necessitates accurately acceptable predictions of water table depth fluctuations. Owing to the difficulties of identifying non-linear model structure and estimating the associated parameters, Back Propagation Neural Network (BPNN) and Radial Basis function network (RBFN) model is taken into account for study. Back propagation neural network model with delta algorithm is calibrated using historical groundwater level records and related hydro-meteorological data to simulate water table fluctuations in the study area. Similarly RBFN network has been used to analyze the water table depth prediction for four different stations. In the present investigation comparative assessment of water table depth for four different stations as well as the sensitivity of above two different models have been identified.