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This study proposes a new groundwater potentiality model (GPM) in the Bisha watershed, Saudi Arabia, by integrating logistic-regression (LR)-weighted and fuzzy logic-based ensemble machine learning (EML) models for the present and future scenarios. We applied random forest, bagging, and random subspace models for predicting the GPMs. We also used the general circulation model’s (GCM) minimum and maximum representative concentration pathway (RCP) 2.6 and 8.5 scenarios for the future GWP mapping. Results showed that the bagging hybrid model (Area under Curve: 0.986) outperformed other models. GWP models predicted 4058.57 km 2  as very high, 4267.45 km 2  as high, 4185.23 km 2  as moderate, 4342. km 2  as low, and 4430.24 km 2  as very low groundwater potential zones. The best model combined with the future climatic conditions shows very high and high groundwater potential zones would cover 2319–2590 km 2  and 3100–2795 km 2 . The current research will aid in the development of long-term sustainable groundwater management plans by increasing the effectiveness of GPMs.

Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed, Saudi Arabia