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Geo-Environmental Risk Assessment of Sand Dunes Encroachment Hazards in Arid Lands Using Machine Learning Techniques

Author

Listed:
  • Ahmed K. Abd El Aal

    (Civil Engineering Department, College of Engineering, Najran University, Najran 1988, Saudi Arabia)

  • Hossam M. GabAllah

    (Geological Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt)

  • Hanaa A. Megahed

    (Geological Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt)

  • Maha K. Selim

    (Geography Department, Faculty of Arts, Cairo University, Giza 12613, Egypt)

  • Mahmoud A. Hegab

    (Mineral Resources Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt)

  • Mohamed E. Fadl

    (Division of Scientific Training and Continuous Studies, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt)

  • Nazih Y. Rebouh

    (Department of Environmental Management, Institute of Environmental Engineering, RUDN University, 6 Miklukho-Maklaya St., Moscow 117198, Russia)

  • Heba El-Bagoury

    (Geography Department, Faculty of Arts, Port Said University, Port Said 42511, Egypt)

Abstract

Machine Learning Techniques (MLTs) and accurate geographic mapping are crucial for managing natural hazards, especially when monitoring the movement of sand dunes. This study presents the integration of MLTs with geographic information systems (GIS) and “R” software to monitor sand dune movement in Najran City, Saudi Arabia (KSA). Utilizing Linear Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN) with nine dune-related variables, this study introduces a new Drifting Sand Index (DSI) for effectively identifying and mapping dune accumulations. The DSI incorporates multispectral sensors data and demonstrates a robust capability for monitoring sand dune dynamics. Field surveys and spatial data analysis were used to identify about 100 dune locations, which were then divided into training (70%) and validation (30%) sets at random. These models produced a thorough dune encroachment risk map that divided areas into five hazard zones: very low, low, medium, high, and very high risk. The results show an average sand dune movement of 0.8 m/year towards the southeast. Performance evaluation utilizing the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) approach revealed AUC values of 96.2% for SVM, 94.2% for RF, and 93% for ANN, indicating RF (AUC = 96.2%) as the most effective MLTs. This crucial information provides valuable insights for sustainable development and environmental protection, enabling decision-makers to prioritize regions for mitigation techniques against sand dune encroachment.

Suggested Citation

  • Ahmed K. Abd El Aal & Hossam M. GabAllah & Hanaa A. Megahed & Maha K. Selim & Mahmoud A. Hegab & Mohamed E. Fadl & Nazih Y. Rebouh & Heba El-Bagoury, 2024. "Geo-Environmental Risk Assessment of Sand Dunes Encroachment Hazards in Arid Lands Using Machine Learning Techniques," Sustainability, MDPI, vol. 16(24), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11139-:d:1547382
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    References listed on IDEAS

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    1. Beatrice Asenso Barnieh & Li Jia & Massimo Menenti & Min Jiang & Jie Zhou & Yelong Zeng & Ali Bennour, 2021. "Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method," Sustainability, MDPI, vol. 13(9), pages 1-37, April.
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