ArticleGround water
PSO-Optimized Ensemble Learning with SHAP for Seasonal Groundwater Quality Prediction.
Article in Ground water. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Groundwater is the principal source of freshwater in hyper-arid regions such as the Algerian Sahara, making reliable groundwater quality assessment essential for sustainable water resource management. This study developed an interpretable hybrid modeling framework for predicting the groundwater WQI in the Ghardaia region by integrating Particle Swarm Optimization (PSO) with ensemble machine learning algorithms. A total of 540 groundwater samples were collected from 67 artesian boreholes during the dry/wet seasons over the period 2021 to 2025. To ensure robust model evaluation and minimize data leakage, a spatially independent validation strategy. Based on borehole locations were adopted. Three ensemble algorithms, namely Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), were optimized using PSO and trained separately for the dry/wet-season datasets. Among the evaluated models, PSO-AdaBoost achieved the best predictive performance during the dry season, with R
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