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Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Author

Listed:
  • Sheikh Kamran Abid

    (KANZU Research: Resilient Built Environment (RBE), Faculty of Technology Management and Business (FPTP), Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Noralfishah Sulaiman

    (KANZU Research: Resilient Built Environment (RBE), Faculty of Technology Management and Business (FPTP), Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Shiau Wei Chan

    (KANZU Research: Resilient Built Environment (RBE), Faculty of Technology Management and Business (FPTP), Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Umber Nazir

    (KANZU Research: Resilient Built Environment (RBE), Faculty of Technology Management and Business (FPTP), Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia)

  • Muhammad Abid

    (College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China)

  • Heesup Han

    (College of Hospitality and Tourism Management, Sejong University, Seoul 05006, Korea)

  • Antonio Ariza-Montes

    (Social Matters Research Group, Universidad Loyola Andalucía, 414004 Córdoba, Spain)

  • Alejandro Vega-Muñoz

    (Public Policy Observatory, Universidad Autónoma de Chile, 425 Pedro de Valdivia Avenue, Santiago 7500912, Chile)

Abstract

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

Suggested Citation

  • Sheikh Kamran Abid & Noralfishah Sulaiman & Shiau Wei Chan & Umber Nazir & Muhammad Abid & Heesup Han & Antonio Ariza-Montes & Alejandro Vega-Muñoz, 2021. "Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12560-:d:678675
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    References listed on IDEAS

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    Cited by:

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    2. S. M. Amin Hosseini & Rama Ghalambordezfooly & Albert de la Fuente, 2022. "Sustainability Model to Select Optimal Site Location for Temporary Housing Units: Combining GIS and the MIVES–Knapsack Model," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    3. Jieyin Lyu & Shouqin Zhou & Jingang Liu & Bingchun Jiang, 2023. "Intelligent-Technology-Empowered Active Emergency Command Strategy for Urban Hazardous Chemical Disaster Management," Sustainability, MDPI, vol. 15(19), pages 1-28, September.
    4. Brielle Lillywhite & Gregor Wolbring, 2022. "Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review," Sustainability, MDPI, vol. 14(20), pages 1-50, October.
    5. Hyun Jin Han & Hae Sun Suh, 2023. "Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach," IJERPH, MDPI, vol. 20(19), pages 1-13, September.

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