Evaluation of GRNN and RBF Model Performance for Groundwater Level Forecasting at Southwest Coast of India

An accurate and reliable forecast plays a vital role for proper planning and utilization of groundwater resources in a sustainable manner. In the present work, an investigation has been carried out in selective wells based on different land use/land cover in the micro watershed located southwest coast of India. The present study utilizes the Generalized Regression Neural Network (GRNN) for forecasting groundwater level (GWL) and compares its performance with that of the Feed Forward Back Propagation (FFBP) trained with Levenberg Marquartz (LM)] and Radial Basis Function (RBF). Weekly time series groundwater level data were used for span of three years (2004-2007). The comparative analysis of the obtained results showed that the GRNN and RBF have the superiority over the FFBP methods for forecasting groundwater level. On the basis of performance criteria (i.e lower RMSE and higher CE), GRNN yielded the better performance to RBF considering the models developed in the study.