{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T10:15:52Z","timestamp":1775211352624,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T00:00:00Z","timestamp":1652572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KU Leuven Belgium and the University of Moulay Ismail Morocco"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid\/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid\/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid\/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.<\/jats:p>","DOI":"10.3390\/rs14102379","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8089-0290","authenticated-orcid":false,"given":"Safae","family":"Ijlil","sequence":"first","affiliation":[{"name":"Laboratory of Geoengineering and Environment, Research Group \u201cWater Sciences and Environment Engineering\u201d, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1112-1783","authenticated-orcid":false,"given":"Ali","family":"Essahlaoui","sequence":"additional","affiliation":[{"name":"Laboratory of Geoengineering and Environment, Research Group \u201cWater Sciences and Environment Engineering\u201d, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0019-6862","authenticated-orcid":false,"given":"Meriame","family":"Mohajane","sequence":"additional","affiliation":[{"name":"Laboratory of Geoengineering and Environment, Research Group \u201cWater Sciences and Environment Engineering\u201d, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"},{"name":"Research Group \u201cSoil and Environment Microbiology\u201d, Department of Biology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"}]},{"given":"Narjisse","family":"Essahlaoui","sequence":"additional","affiliation":[{"name":"Laboratory of Geoengineering and Environment, Research Group \u201cWater Sciences and Environment Engineering\u201d, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"}]},{"given":"El Mostafa","family":"Mili","sequence":"additional","affiliation":[{"name":"Laboratory of Geoengineering and Environment, Research Group \u201cWater Sciences and Environment Engineering\u201d, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5435-6887","authenticated-orcid":false,"given":"Anton","family":"Van Rompaey","sequence":"additional","affiliation":[{"name":"Geography and Tourism Research Group, Department Earth and Environmental Science, KU Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100466","DOI":"10.1016\/j.gsd.2020.100466","article-title":"A Fuzzy Based MCDM\u2013GIS Framework to Evaluate Groundwater Potential Index for Sustainable Groundwater Management\u2014A Case Study in an Urban-Periurban Ensemble, Southern India","volume":"11","author":"Jesiya","year":"2020","journal-title":"Groundw. 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