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A New Decision Support System for Analyzing Factors of Tornado Related Deaths in Bangladesh

Author

Listed:
  • Fahim Sufi

    (Federal Government, Melbourne, VIC 3000, Australia)

  • Edris Alam

    (Faculty of Resilience, Rabdan Academy, P.O. Box 114646, Abu Dhabi 22401, United Arab Emirates
    Department of Geography and Environmental Studies, University of Chittagong, Chittagong 4331, Bangladesh)

  • Musleh Alsulami

    (Information Systems Department, Umm Al-Qura University (UQU), Makkah 24382, Saudi Arabia)

Abstract

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new Tornado catalogue that include present and past records of Tornados across Bangladesh maximizing use of available sources. Within this new Tornado database, 119 records were captured starting from 1838 till 2020 causing 8735 deaths and 97,868 injuries leaving more than 102,776 people affected in total. Moreover, using this new Tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of Tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the Tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.

Suggested Citation

  • Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "A New Decision Support System for Analyzing Factors of Tornado Related Deaths in Bangladesh," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6303-:d:821074
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    References listed on IDEAS

    as
    1. Elsner, James B. & Schroder, Zoe, 2019. "Tornado damage ratings estimated with cumulative logistic regression," Earth Arxiv k9wv6, Center for Open Science.
    2. Fuad Aleskerov & Sergey Demin & Michael B. Richman & Sergey Shvydun & Theodore B. Trafalis & Vyacheslav Yakuba, 2020. "Constructing an Efficient Machine Learning Model for Tornado Prediction," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1177-1187, August.
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    Cited by:

    1. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "Automated Analysis of Australian Tropical Cyclones with Regression, Clustering and Convolutional Neural Network," Sustainability, MDPI, vol. 14(16), pages 1-23, August.

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    1. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "Automated Analysis of Australian Tropical Cyclones with Regression, Clustering and Convolutional Neural Network," Sustainability, MDPI, vol. 14(16), pages 1-23, August.

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