DRASTIC-FS Model for Assessment of Groundwater Vulnerability in Alluvial Region between Mahi and Narmada Rivers, India

Abstract

ABSTRACT Vulnerability of groundwater to contamination in alluvial region between Mahi and Narmada rivers of Gujarat has been assessed in present study. There have been numerous studies that have analyzed DRASTIC models with weights suggested by the Delphi committee. These models have some limitations that have been overcome by optimizing the weights using artificial neural networks (ANNs). The “DRASTIC-FS” model has an additional layer that takes into account the impact of the physico-chemical properties of groundwater. The dominating factor scores (FS) at each well location were derived from the statistical analysis of the quality dataset. Factor score’s rating and the weight have been assigned considering to its intervention and influence to vulnerability. The DRASTIC and DRASTIC-FS models were carried out in GIS environment to obtain vulnerability maps for 2018 pre and post monsoon seasons. The weights of variable parameters (i.e., D, R, I, C, and FS) have also been optimized using ANN (Artificial Neural Network) and the results were compared with the effective weights derived from SPSA method of sensitivity analysis. Using ANN optimized weights and groundwater nitrate concentrations, the vulnerability maps were validated. Vulnerability maps of DRASTIC-FS with ANN weights correlated well for the year 2018, 2019 and 2020 (Pearson’s ‘r’= 0.56, 0.65 and 0.50 respectively) with groundwater Nitrate concentrations proving its higher efficiency over the DRASTIC method (Pearson’s ‘r’= 0.36, 0.48 and 0.32).