Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning

Abstract

Nitrate contamination in groundwater was evaluated using the concept of integrated aquifer assessment by combining groundwater characterization and risk analysis with tiered approaches for land and surface runoff contamination by soil chemicals and leaching of contamination to groundwater in the Upper White River Watershed (UWRW) in Indiana. Integrated aquifer vulnerability assessment was conducted using an integration of a distributed watershed model (Soil and Water Assessment Tool [SWAT]) and a machine learning technique (Geospatial-Artificial Neural Network [Geo-ANN]). The results indicate that integrated aquifer vulnerability assessment performed well based on the model performance (NSE/R2/PBIAS = 0.66/0.70/0.07). Thus, the overall assessment of aquifer vulnerability can be performed using the integrated aquifer vulnerability assessment technique provided in this study. Moreover, this approach provides an efficient guide for managing groundwater resources for policy makers and groundwater-related researchers.