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

Ahmed K. Abd El Aal, Hossam M. GabAllah, Hanaa A. Megahed, Maha K. Selim, Mahmoud A. Hegab, Mohamed E. Fadl (), Nazih Y. Rebouh () and Heba El-Bagoury
Additional contact information
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

Sustainability, 2024, vol. 16, issue 24, 1-23

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.

Keywords: Sand dunes; DSI; MLTs; geo-environmental risk; Najran city; arid lands (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/24/11139/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/24/11139/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:24:p:11139-:d:1547382

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().