Innovative Machine Learning, Isotopic, and Hydrogeochemical Techniques for Groundwater Analysis in Arid Landscapes in Egypt’s Eastern Desert

Abstract Groundwater serves as a lifeline in Egypt’s hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (δ¹⁸O and δ²H), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and isotopic signatures distinguish meteoric water from paleowater sources, revealing significant mixing and recharge processes. The findings indicate that the Quaternary aquifer is increasingly influenced by upward leakage from the Nubian aquifer, facilitated by deep-seated faults. Between 2014 and 2021, water levels in the Quaternary aquifer declined by up to 14 m due to over-extraction, particularly in agricultural zones. To enhance predictive capabilities, a Support Vector Machine (SVM) model was developed to estimate δ¹⁸O values using multiple hydrochemical indicators, achieving strong performance (R² = 0.92, MSE = 2.89). SHapley Additive exPlanations (SHAP) analysis identified Mg²⁺, HCO₃⁻, and SO₄²⁻ as dominant factors influencing isotope variation. This integrated approach represents a novel application of explainable machine learning in hydrogeology and offers a scalable, cost-effective tool for assessing groundwater systems in arid regions. The study contributes directly to national water security goals and supports the global Sustainable Development Goal 6 (SDG 6) for clean water and sanitation.