A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models.

Abstract

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas.