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Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

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  • Seth Guikema

Abstract

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI‐based approach for disaster risk analysis that we as a society must be cautious of?

Suggested Citation

  • Seth Guikema, 2020. "Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1117-1123, June.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:6:p:1117-1123
    DOI: 10.1111/risa.13476
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    References listed on IDEAS

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    1. Guikema, Seth D., 2009. "Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 855-860.
    2. Baroud, Hiba & Barker, Kash, 2018. "A Bayesian kernel approach to modeling resilience-based network component importance," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 10-19.
    3. Sarah LaRocca & Jonas Johansson & Henrik Hassel & Seth Guikema, 2015. "Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 608-623, April.
    4. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
    5. Aven, Terje, 2013. "A conceptual framework for linking risk and the elements of the data–information–knowledge–wisdom (DIKW) hierarchy," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 30-36.
    6. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
    7. Aven, Terje, 2017. "Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 42-48.
    8. Winkler, James & Dueñas-Osorio, Leonardo & Stein, Robert & Subramanian, Devika, 2010. "Performance assessment of topologically diverse power systems subjected to hurricane events," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 323-336.
    9. Han, Seung-Ryong & Guikema, Seth D. & Quiring, Steven M. & Lee, Kyung-Ho & Rosowsky, David & Davidson, Rachel A., 2009. "Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 199-210.
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    Cited by:

    1. Hafiz Suliman Munawar & Ahmed W. A. Hammad & S. Travis Waller & Muhammad Jamaluddin Thaheem & Asheem Shrestha, 2021. "An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    2. Terje Aven & Roger Flage, 2020. "Foundational Challenges for Advancing the Field and Discipline of Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2128-2136, November.

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