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A simple Monte Carlo method for estimating the chance of a cyclone impact

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  • Xiaoliang Xie

    (Hunan University of Technology and Business)

  • Bingqi Xie

    (Hunan University of Technology and Business)

  • Jiaqi Cheng

    (Hunan University of Technology and Business)

  • Qi Chu

    (Hunan University of Technology and Business)

  • Thomas Dooling

    (University of North Carolina Pembroke)

Abstract

Cyclones endanger life and cause great financial impact on interior and coastal regions through the destruction of buildings and land. Governments need to have a way of estimating the chance of different regions being impacted by a cyclone. The goal of this paper is to use big data to better predict future cyclone impacts. Large cyclone data sets from the CMA Tropical Cyclone Data Center are used in the analysis. By using big data analysis techniques, long-term patterns in cyclone locations and size can be revealed. The Hausdorff distance is used to determine overall changes in cyclone positions decade by decade. Monte Carlo techniques estimate the probability of a region being impacted by a cyclone any given year. This is done by creating random data sets that mimic long-term patterns in cyclone position and radii. It will be shown that any region can be assigned a probability of cyclone impact purely on large historical data sets.

Suggested Citation

  • Xiaoliang Xie & Bingqi Xie & Jiaqi Cheng & Qi Chu & Thomas Dooling, 2021. "A simple Monte Carlo method for estimating the chance of a cyclone impact," 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. 107(3), pages 2573-2582, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04505-2
    DOI: 10.1007/s11069-021-04505-2
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    References listed on IDEAS

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    1. Solomon M. Hsiang & Amir S. Jina, 2014. "The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 Cyclones," NBER Working Papers 20352, National Bureau of Economic Research, Inc.
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    3. Naif Al Mudawi & Mahwish Pervaiz & Bayan Ibrahimm Alabduallah & Abdulwahab Alazeb & Abdullah Alshahrani & Saud S. Alotaibi & Ahmad Jalal, 2023. "Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors," Sustainability, MDPI, vol. 15(20), pages 1-18, October.

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    Keywords

    Cyclone; Impact; Probability;
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