IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10388-d1184532.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10388/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/10388/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
    2. Abhinash Jenasamanta & Subrajeet Mohapatra, 2022. "An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    3. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.
    4. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    5. Rashinda Wijethunga & Hooman Nouraei & Craig Zych & Jagath Samarabandu & Ayan Sadhu, 2024. "Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding," Energies, MDPI, vol. 17(3), pages 1-23, February.
    6. Rabin Chakrabortty & Subodh Chandra Pal & Alireza Arabameri & Phuong Thao Thi Ngo & Indrajit Chowdhuri & Paramita Roy & Sadhan Malik & Biswajit Das, 2022. "Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3518-3557, March.
    7. Atul Kumar & Sunil Singh & Malay Pramanik & Shairy Chaudhary & Ashwani Kumar Maurya & Manoj Kumar, 2022. "Watershed prioritization for soil erosion mapping in the Lesser Himalayan Indian basin using PCA and WSA methods in conjunction with morphometric parameters and GIS-based approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3723-3761, March.
    8. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    9. Herrera, Rubén & Climent, Francisco & Carmona, Pedro & Momparler, Alexandre, 2022. "The manipulation of Euribor: An analysis with machine learning classification techniques," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    10. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    11. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    12. Paolenmang Haokip & Md. Abdullah Khan & Pandurang Choudhari & Luc Cimusa Kulimushi & Ibodullo Qaraev, 2022. "Identification of erosion-prone areas using morphometric parameters, land use land cover and multi-criteria decision-making method: geo-informatics approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 527-557, January.
    13. Jamei, Mehdi & Karbasi, Masoud & Malik, Anurag & Jamei, Mozhdeh & Kisi, Ozgur & Yaseen, Zaher Mundher, 2022. "Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms," Agricultural Water Management, Elsevier, vol. 269(C).
    14. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    15. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    16. Kocaarslan, Baris & Soytas, Ugur, 2023. "The role of major markets in predicting the U.S. municipal green bond market performance: New evidence from machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10388-:d:1184532. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.