Groundwater Salinity Prediction in Deep Desert-Stressed Aquifers Using a Novel Multi-Stage Modeling Framework Integrating Enhanced Ensemble Learning and Hybrid AI Techniques

International audience Salinization of deep groundwater is a significant environmental and economic challenge in arid and desert zones, driven by both natural processes and human activities. Understanding the causes and dynamics of groundwater salinity is essential for protecting water quality and ensuring sustainable resource use. This study presents a novel approach, using hybrid artificial intelligence methods built upon enhanced ensemble decision tree models (EdTE-ML), including CatBoost (CatBR-m), ExtraTrees (ExTR-m), and custom Bootstrapping Regressor (BsTR-m), within a two-stage predictive framework. This study focuses on a deep, stressed aquifer in the oasis zone of Kebili, in southwestern Tunisia's desert region. In the first stage, CatBR-m and ExTR-m served as base models, generating predictive features for the BsTR-m model in the second stage. Despite relying on limited hydrochemical data from a small number of wells, both base models produced satisfactory results. The BsTR-m model in the second stage outperformed individual models in terms of accuracy, generalization to unseen data, and spatial identification of salinity-affected zones. The proposed methodology accurately predicts groundwater salinity levels, providing an effective tool for early detection of water quality degradation. This predictive capability supports more proactive and sustainable groundwater management strategies in vulnerable desert aquifer systems.