Geo-AI Ensemble Modeling Framework for Assessing Groundwater Contamination Under Anthropogenic Pressures in an Extensive Peri-Urban Agricultural Aquifer to Support Sustainable Groundwater Management

Abstract

Rapid urbanisation and intensified agriculture are major drivers of groundwater contamination in peri-urban agricultural aquifers worldwide. Contaminants including nitrates and phosphates accumulate through fertilizer use, wastewater infiltration, and groundwater overextraction, creating complex spatial and temporal patterns. Quantifying these impacts under multiple anthropogenic pressures remains a key challenge for effective water resource management. This study develops a Geo-AI ensemble modeling framework that integrates grid-based spatial analysis with advanced machine learning to assess groundwater contamination dynamics. A composite contamination index (CCI) was constructed to synthesize hydrochemical indicators into a unified measure of aquifer degradation. The AI framework uses Graph Neural Networks (GNNs), Light Gradient Boosting Machine (LightGBM), and Deep Long Short-Term Memory Networks (LSTM). Anthropogenic drivers include population growth, infrastructure density, agricultural intensity, groundwater abstraction, and hydroclimatic variability, providing a comprehensive understanding of contamination sources. The methodology was applied to the urbanised aquifer of Manouba, western suburban Tunis (Tunisia), using 295 samples collected from 85 monitoring wells between 2005 and 2025. Validation results show strong predictive performance, with LightGBM achieving R2 = 0.986, RMSE = 13.14, and MAE = 1.72, outperforming GNNs (R2 = 0.972) and LSTM (R2 = 0.943). The spatial analysis reveals a major shift in contamination patterns, with severe contamination expanding to 55% of the study area in 2025, compared with 7% in 2005, while low and slight contamination declined from 45% to 20%. The results highlight how urban expansion reduces recharge, increases pollutant loading, and amplifies aquifer vulnerability, while agricultural intensification further accelerates contaminant accumulation and degradation processes. This framework provides a transferable, data-driven tool for mapping contamination hotspots and supporting targeted, sustainable groundwater management in peri-urban agricultural aquifers under increasing anthropogenic pressures worldwide.