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Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Author

Listed:
  • Lamya Ouali

    (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, BP 509 Boutalamine, Errachidia 52000, Morocco)

  • Lahcen Kabiri

    (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, BP 509 Boutalamine, Errachidia 52000, Morocco)

  • Mustapha Namous

    (Laboratory of Data Science for Sustainable Earth, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco)

  • Mohammed Hssaisoune

    (Laboratory of Applied Geology and Geo-Environment, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco
    Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul 86150, Morocco)

  • Kamal Abdelrahman

    (Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Mohammed S. Fnais

    (Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Hichame Kabiri

    (Laboratory of Artificial Intelligence, Faculty of Sciences, Moulay Ismail University, Meknes BP11201, Morocco)

  • Mohammed El Hafyani

    (Laboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes BP11201, Morocco
    ULiège (Gembloux Agro-Bio Tech), Terra Research Center, Water-Soil-Plant Exchange, 5030 Gembloux, Belgium)

  • Hassane Oubaassine

    (Laboratory of the Dynamics of the Lithosphere and the Genesis of Resources, Faculty of Sciences-Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco)

  • Abdelkrim Arioua

    (Water Resources Management and Valorization and Remote Sensing Team, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco)

  • Lhoussaine Bouchaou

    (Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul 86150, Morocco
    International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco)

Abstract

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.

Suggested Citation

  • Lamya Ouali & Lahcen Kabiri & Mustapha Namous & Mohammed Hssaisoune & Kamal Abdelrahman & Mohammed S. Fnais & Hichame Kabiri & Mohammed El Hafyani & Hassane Oubaassine & Abdelkrim Arioua & Lhoussaine , 2023. "Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco," Sustainability, MDPI, vol. 15(5), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3874-:d:1075321
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    References listed on IDEAS

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    1. Hiroki Amano & Yoichiro Iwasaki, 2020. "Land Cover Classification by Integrating NDVI Time Series and GIS Data to Evaluate Water Circulation in Aso Caldera, Japan," IJERPH, MDPI, vol. 17(18), pages 1-18, September.
    2. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    3. Giacomo Medici & Jeff B. Langman, 2022. "Pathways and Estimate of Aquifer Recharge in a Flood Basalt Terrain; A Review from the South Fork Palouse River Basin (Columbia River Plateau, USA)," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
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