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August 19, 2025WaterOpen Access

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

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Novel modeling predicts groundwater salinity in stressed aquifers, suggesting enhanced management strategies.

The predictive capability of the BsTR-m model accurately identifies salinity levels in groundwater, enabling proactive management.
Enhanced ensemble learning techniques improved prediction accuracy, with CatBR-m and ExTR-m used to generate features for model enhancement.
A two-stage predictive framework was implemented, utilizing limited hydrochemical data, yet achieving satisfactory results in salinity prediction.
This methodology provides a tool for early detection of water quality degradation in vulnerable desert aquifers, addressing environmental challenges.

Msaddek et al. (2025) studied this question.

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