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Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support

Author

Listed:
  • Saad Mazhar Khan

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Imran Shafi

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Wasi Haider Butt

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Isabel de la Torre Díez

    (Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Miguel Angel López Flores

    (Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Research Group on Foods, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Instituto Politécnico Nacional, UPIICSA, Ciudad de México 04510, Mexico)

  • Juan Castañedo Galvlán

    (Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Universidad Internacional Iberoamericana, Arecibo 00613, Puerto Rico
    Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola)

  • Imran Ashraf

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea)

Abstract

Society and the environment are severely impacted by catastrophic events, specifically floods. Inadequate emergency preparedness and response are frequently the result of the absence of a comprehensive plan for flood management. This article proposes a novel flood disaster management (FDM) system using the full lifecycle disaster event model (FLCNDEM), an abstract model based on the function super object. The proposed FDM system integrates data from existing flood protocols, languages, and patterns and analyzes viewing requests at various phases of an event to enhance preparedness and response. The construction of a task library and knowledge base to initialize FLCNDEM results in FLCDEM flooding response. The proposed FDM system improves the emergency response by offering a comprehensive framework for flood management, including pre-disaster planning, real-time monitoring, and post-disaster evaluation. The proposed system can be modified to accommodate various flood scenarios and enhance global flood management.

Suggested Citation

  • Saad Mazhar Khan & Imran Shafi & Wasi Haider Butt & Isabel de la Torre Díez & Miguel Angel López Flores & Juan Castañedo Galvlán & Imran Ashraf, 2023. "Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support," Land, MDPI, vol. 12(8), pages 1-27, August.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1538-:d:1209658
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    References listed on IDEAS

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    1. V. Daksiya & H. T. Su & Y. H. Chang & Edmond Y. M. Lo, 2017. "Incorporating socio-economic effects and uncertain rainfall in flood mitigation decision using MCDA," 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. 87(1), pages 515-531, May.
    2. Heather McGrath & Jean-François Bourgon & Jean-Samuel Proulx-Bourque & Miroslav Nastev & Ahmad Abo El Ezz, 2018. "A comparison of simplified conceptual models for rapid web-based flood inundation mapping," 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. 93(2), pages 905-920, September.
    3. Stefanos Stefanidis & Dimitrios Stathis, 2013. "Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP)," 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. 68(2), pages 569-585, September.
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    Cited by:

    1. Syed Asad Shabbir Bukhari & Imran Shafi & Jamil Ahmad & Hammad Tanveer Butt & Tahir Khurshaid & Imran Ashraf, 2025. "Enhancing flood monitoring and prevention using machine learning and IoT integration," 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. 121(4), pages 4837-4864, March.
    2. Syed Asad Shabbir Bukhari & Imran Shafi & Jamil Ahmad & Santos Gracia Villar & Eduardo Garcia Villena & Tahir Khurshaid & Imran Ashraf, 2025. "Review of flood monitoring and prevention approaches: a data analytic perspective," 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. 121(5), pages 5103-5128, March.

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