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Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria

Author

Listed:
  • Hamza Bouguerra

    (Water Resources and Sustainable Development Laboratory, Department of Geology, Faculty of Earth Sciences, Badji Mokhtar—Annaba University, P.O. Box 12, Annaba 23000, Algeria)

  • Salah Eddine Tachi

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Hamza Bouchehed

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Gordon Gilja

    (Department of Hydroscience and Engineering, Faculty of Civil Engineering, University of Zagreb, Kaciceva 26, HR-10000 Zagreb, Croatia)

  • Nadir Aloui

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Yacine Hasnaoui

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Abdelmalek Aliche

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Saâdia Benmamar

    (Department of Hydraulics, Laboratoire de Recherche des Sciences de L’eau, National Polytechnic School, 10 Rue des Frères OUDEK, El Harrach, Algiers 16200, Algeria)

  • Jose Navarro-Pedreño

    (Department of Agrochemistry and Environment, University Miguel Hernández of Elche, 03202 Elche, Spain)

Abstract

Erosion can have a negative impact on the agricultural sustainability and grazing lands in the Mediterranean area, especially in northern Algeria. It is useful to map the spatial occurrence of erosion and identify susceptible erodible areas on large scale. The main objective of this research was to compare the performance of four machine learning techniques: Categorical boosting, Adaptive boosting, Convolutional Neural Network, and stacking ensemble models to predict the occurrence of erosion in the Macta basin, northwestern Algeria. Several climatologic, morphologic, hydrological, and geological factors based on multi-sources data were elaborated in GIS environment to determine the erosion factors in the studied area. The conditioning factors encompassing rainfall erosivity, slope, aspect, elevation, LULC, topographic wetness index, distance from river, distance from roads, clay mineral ratio, lithology, and geology were derived via the integration of topographic attributes and remote sensing data including Landsat 8 and Sentinel 2 within a GIS framework. The inventory map of soil erosion was created by integrating data from the global positioning system to locate erosion sites, conducting extensive field surveys, and analyzing satellite images obtained from Google Earth through visual interpretation. The dataset was divided randomly into two sets with 60% for training and calibrating and 40% for testing the models. Statistical metrics including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (ROC) were used to assess the validity of the proposed models. The results revealed that machine learning and deep learning, as well stacking ensemble techniques, showed outstanding performance with accuracy over 98% with sensitivity 0.98 and specificity 0.98. Policy makers and local authorities can utilize the predicted erosion susceptibility maps to promote sustainable use of water and soil conservation and safeguard agricultural activities against potential damage.

Suggested Citation

  • Hamza Bouguerra & Salah Eddine Tachi & Hamza Bouchehed & Gordon Gilja & Nadir Aloui & Yacine Hasnaoui & Abdelmalek Aliche & Saâdia Benmamar & Jose Navarro-Pedreño, 2023. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10388-:d:1184532
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    References listed on IDEAS

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    1. Tusar Kanti Hembram & Sunil Saha, 2020. "Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 1241-1268, February.
    2. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Penélope Mostazo & Carlos Asensio-Amador & Carlos Asensio, 2023. "Soil Erosion Modeling and Monitoring," Agriculture, MDPI, vol. 13(2), pages 1-4, February.
    4. Elsayed A. Abdelsamie & Mostafa A. Abdellatif & Farag O. Hassan & Ahmed A. El Baroudy & Elsayed Said Mohamed & Dmitry E. Kucher & Mohamed S. Shokr, 2022. "Integration of RUSLE Model, Remote Sensing and GIS Techniques for Assessing Soil Erosion Hazards in Arid Zones," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
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    1. Yang Liu & Tianxing Yang & Liwei Tian & Bincheng Huang & Jiaming Yang & Zihan Zeng, 2024. "Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    2. Obasi S.N & Tenebe V. A & Obasi C.C & Jokthan G.E & Adjei E.A & Keyagha E.R, 2024. "Harnessing Artificial Intelligence For Sustainable Agriculture: A Comprehensive Review Of African Applications In Spatial Analysis And Precision Agriculture," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 6(1), pages 01-13, April.

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