groundwater potential; machine learning; oasis; performance; spatial prediction; water supply; Computer Science (miscellaneous); Geography, Planning and Development; Renewable Energy, Sustainability and the Environment; Building and Construction; Environmental Science (miscellaneous); Energy Engineering and Power Technology; Hardware and Architecture; Computer Networks and Communications; Management, Monitoring, Policy and Law
Abstract :
[en] Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Ouali, Lamya ; Laboratory of Engineering Sciences and Techniques, Geo-Resource Geo-Environment Geological and Oasis Heritage Research Team, Department of Geosciences, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco
Kabiri, Lahcen; Laboratory of Engineering Sciences and Techniques, Geo-Resource Geo-Environment Geological and Oasis Heritage Research Team, Department of Geosciences, Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco
Namous, Mustapha ; Laboratory of Data Science for Sustainable Earth, Sultan Moulay Slimane University, Beni Mellal, Morocco
Hssaisoune, Mohammed ; Laboratory of Applied Geology and Geo-Environment, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco ; Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul, Morocco
Abdelrahman, Kamal ; Department of Geology & Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia
Fnais, Mohammed S.; Department of Geology & Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia
Kabiri, Hichame; Laboratory of Artificial Intelligence, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
El Hafyani, Mohammed ; Université de Liège - ULiège > Département GxABT > Echanges Eau - Sol - Plantes
Oubaassine, Hassane; Laboratory of the Dynamics of the Lithosphere and the Genesis of Resources, Faculty of Sciences-Semlalia, Cadi Ayyad University, Marrakech, Morocco
Arioua, Abdelkrim; Water Resources Management and Valorization and Remote Sensing Team, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal, Morocco
Bouchaou, Lhoussaine ; Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul, Morocco ; International Water Research Institute, VI Polytechnic University, Ben Guerir, Morocco
Language :
English
Title :
Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco
Deep thanks and gratitude to the Researchers Supporting Project number (RSP2023R351), King Saud University, Riyadh, Saudi Arabia for supporting this research article. The authors are grateful to the ABHGZR, ORMVAO, and ONEE for the supply of the necessary data, and they also wish to thank the three anonymous reviewers for their constructive comments.
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