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
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...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/10/2379 |
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| author | Safae Ijlil Ali Essahlaoui Meriame Mohajane Narjisse Essahlaoui El Mostafa Mili Anton Van Rompaey |
| author_facet | Safae Ijlil Ali Essahlaoui Meriame Mohajane Narjisse Essahlaoui El Mostafa Mili Anton Van Rompaey |
| author_sort | Safae Ijlil |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:56:53Z |
| format | Article |
| id | doaj.art-6b9acb2dea8546248e83af92f3985160 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:56:53Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6b9acb2dea8546248e83af92f39851602023-11-23T12:55:17ZengMDPI AGRemote Sensing2072-42922022-05-011410237910.3390/rs14102379Machine 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 SystemSafae Ijlil0Ali Essahlaoui1Meriame Mohajane2Narjisse Essahlaoui3El Mostafa Mili4Anton Van Rompaey5Laboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, MoroccoLaboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, MoroccoLaboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, MoroccoLaboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, MoroccoLaboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes B.P.11201, MoroccoGeography and Tourism Research Group, Department Earth and Environmental Science, KU Leuven, Celestijnenlaan 200E, 3001 Heverlee, BelgiumGroundwater 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.https://www.mdpi.com/2072-4292/14/10/2379groundwater pollution riskvulnerabilityDRASTICmachine learning algorithmsbivariate statisticSaiss basin |
| spellingShingle | Safae Ijlil Ali Essahlaoui Meriame Mohajane Narjisse Essahlaoui El Mostafa Mili Anton Van Rompaey 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 Remote Sensing groundwater pollution risk vulnerability DRASTIC machine learning algorithms bivariate statistic Saiss basin |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | groundwater pollution risk vulnerability DRASTIC machine learning algorithms bivariate statistic Saiss basin |
| url | https://www.mdpi.com/2072-4292/14/10/2379 |
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