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Tsunami Flow Characteristics on the East Coast of the UAE by One-Dimensional Numerical Analysis and Artificial Neural Networking

Author

Listed:
  • Napayalage A. K. Nandasena

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Ashraf Hefny

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Cheng Chen

    (College of Civil Engineering, Fuzhou University, Fuzhou 350108, China)

  • Maryam Alshehhi

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Noura Alahbabi

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Fatima Alketbi

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Maha Ali

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Noura Alblooshi

    (Department of Civil and Environmental Engineering, The National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

Abstract

The coastal developments in the Middle East put low priority on tsunami risk assessment due to the rare occurrence and absence of genuine tsunami track records on the coastline in the past. Tsunami-vulnerable coasts, including the east coast of the UAE, need to prepare for, and pay attention to, the impact of future tsunamis due to increased earthquake activity in the region. This study investigated the tsunami characteristics of the nearshore from hypothetical tsunami conditions by applications of numerical modeling and Artificial Neural Network (ANN) methods. The modeling results showed that the maximum tsunami depth at the shore was highest in Khor Fakkan and Mirbih for the given tsunami boundary conditions, while the tsunami withdrawal was greater on the southern bathymetry compared to that on the northern bathymetry when the tsunami period increased. ANN results confirmed that the still sea depth and seabed slope were more important than the tsunami period when predicting the maximum tsunami depth at the shore.

Suggested Citation

  • Napayalage A. K. Nandasena & Ashraf Hefny & Cheng Chen & Maryam Alshehhi & Noura Alahbabi & Fatima Alketbi & Maha Ali & Noura Alblooshi, 2025. "Tsunami Flow Characteristics on the East Coast of the UAE by One-Dimensional Numerical Analysis and Artificial Neural Networking," Sustainability, MDPI, vol. 17(15), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:7036-:d:1716469
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    References listed on IDEAS

    as
    1. G. Hoffmann & K. Reicherter & T. Wiatr & C. Grützner & T. Rausch, 2013. "Block and boulder accumulations along the coastline between Fins and Sur (Sultanate of Oman): tsunamigenic remains?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 851-873, January.
    2. Mouloud Hamidatou & Abdulla Almandous & Khalifa Alebri & Badr Alameri & Ali Megahed, 2024. "Deterministic Tsunami Hazard Assessment for the Eastern Coast of the United Arab Emirates: Insights from the Makran Subduction Zone," Sustainability, MDPI, vol. 16(23), pages 1-33, December.
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