TYPE Original Research PUBLISHED 13 December 2023 DOI 10.3389/frwa.2023.1305998 OPEN ACCESS EDITED BY Francesco Granata, University of Cassino, Italy REVIEWED BY Fabio Di Nunno, University of Cassino, Italy Chaitanya B. Pande, Indian Institute of Tropical Meteorology (IITM), India *CORRESPONDENCE Soufiane Hajaj soufianehajaj13@gmail.com Abdessamad Jari jarigeominesfpt@gmail.com RECEIVED 02 October 2023 ACCEPTED 20 November 2023 PUBLISHED 13 December 2023 CITATION Jari A, Bachaoui EM, Hajaj S, Khaddari A, Khandouch Y, El Harti A, Jellouli A and Namous M (2023) Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco. Front. Water 5:1305998. doi: 10.3389/frwa.2023.1305998 COPYRIGHT © 2023 Jari, Bachaoui, Hajaj, Khaddari, Khandouch, El Harti, Jellouli and Namous. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco Abdessamad Jari 1 *, El Mostafa Bachaoui 1 , Soufiane Hajaj 1 *, Achraf Khaddari 2 , Younes Khandouch 3 , Abderrazak El Harti 1 , Amine Jellouli 1 and Mustapha Namous 4 1 Geomatics, Georesources and Environment Laboratory, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco, 2 Laboratory of Geosciences, Department of Geology, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco, 3 Laboratory of Metrology and Information Processing, Physics Department, Ibn Zohr University, Agadir, Morocco, 4 Laboratory of Data Science for Sustainable Earth, Sultan Moulay Slimane University, Beni Mellal, Morocco Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan- Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating Frontiers in Water 01 frontiersin.org