Preprints
https://doi.org/10.5194/nhess-2022-90
https://doi.org/10.5194/nhess-2022-90
 
01 Apr 2022
01 Apr 2022
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

A Scenario-based Case Study: AI to analyse casualties from landslides in Chittagong Metropolitan Area, Bangladesh

Fahim Sufi1, Edris Alam2,3, and Abu Islam4 Fahim Sufi et al.
  • 1Federal Government, Melbourne, Australia, VIC 3000
  • 2Business Continuity Management & Integrated Emergency Management, Rabdan Academy, Abu Dhabi, UAE
  • 3Department of Geography and Environmental Studies, University of Chittagong, Chittagong-4331, Bangladesh
  • 4Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

Abstract. Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decision that saves lives and reduces the economic impact on society. Using the landslide inventory of Chittagong Metropolitan Area (CMA), we created a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054X1041 possible scenario sets and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines performing both linear and logistic regressions. According to literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted to strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations whereby 91.67 % of users found the solution easy to use, effective and self-explanatory while using via mobile.

Fahim Sufi et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on nhess-2022-90', Edris Alam, 05 Apr 2022
  • CC1: 'Comment on nhess-2022-90', Fatima Zohra, 09 Apr 2022
    • CC3: 'Reply on CC1', Fahim Sufi, 11 Apr 2022
  • CC2: 'Comment on nhess-2022-90', Abu Reza Md. Towfiqul Islam, 10 Apr 2022
  • CC4: 'Comment on nhess-2022-90', Disaster Development, 13 Apr 2022
    • CC5: 'Reply on CC4', Fahim Sufi, 25 Apr 2022
  • CC6: 'Comment on nhess-2022-90', Fahim Sufi, 07 Jun 2022
  • AC2: 'Comment on nhess-2022-90', Edris Alam, 20 Jul 2022
  • RC1: 'Comment on nhess-2022-90', Anonymous Referee #1, 27 Sep 2022
    • AC3: 'Reply on RC1', Edris Alam, 27 Sep 2022
    • CC7: 'Reply on RC1', Fahim Sufi, 28 Sep 2022
  • RC2: 'Comment on nhess-2022-90', Anonymous Referee #2, 05 Oct 2022
    • AC4: 'Reply on RC2', Edris Alam, 08 Oct 2022
      • RC3: 'Reply on AC4', Anonymous Referee #2, 11 Oct 2022
        • AC5: 'Reply on RC3', Edris Alam, 11 Oct 2022
        • AC8: 'Reply on RC3', Edris Alam, 11 Oct 2022
    • AC6: 'Reply on RC2', Edris Alam, 11 Oct 2022
    • AC7: 'Reply on RC2', Edris Alam, 11 Oct 2022
  • RC4: 'FINAL Comment on nhess-2022-90', Anonymous Referee #2, 14 Oct 2022
    • AC11: 'Reply on RC4', Edris Alam, 02 Nov 2022
  • RC5: 'Comment on nhess-2022-90', Anonymous Referee #3, 18 Oct 2022
    • AC9: 'Reply on RC5', Edris Alam, 01 Nov 2022
    • AC10: 'Reply on RC5', Edris Alam, 01 Nov 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on nhess-2022-90', Edris Alam, 05 Apr 2022
  • CC1: 'Comment on nhess-2022-90', Fatima Zohra, 09 Apr 2022
    • CC3: 'Reply on CC1', Fahim Sufi, 11 Apr 2022
  • CC2: 'Comment on nhess-2022-90', Abu Reza Md. Towfiqul Islam, 10 Apr 2022
  • CC4: 'Comment on nhess-2022-90', Disaster Development, 13 Apr 2022
    • CC5: 'Reply on CC4', Fahim Sufi, 25 Apr 2022
  • CC6: 'Comment on nhess-2022-90', Fahim Sufi, 07 Jun 2022
  • AC2: 'Comment on nhess-2022-90', Edris Alam, 20 Jul 2022
  • RC1: 'Comment on nhess-2022-90', Anonymous Referee #1, 27 Sep 2022
    • AC3: 'Reply on RC1', Edris Alam, 27 Sep 2022
    • CC7: 'Reply on RC1', Fahim Sufi, 28 Sep 2022
  • RC2: 'Comment on nhess-2022-90', Anonymous Referee #2, 05 Oct 2022
    • AC4: 'Reply on RC2', Edris Alam, 08 Oct 2022
      • RC3: 'Reply on AC4', Anonymous Referee #2, 11 Oct 2022
        • AC5: 'Reply on RC3', Edris Alam, 11 Oct 2022
        • AC8: 'Reply on RC3', Edris Alam, 11 Oct 2022
    • AC6: 'Reply on RC2', Edris Alam, 11 Oct 2022
    • AC7: 'Reply on RC2', Edris Alam, 11 Oct 2022
  • RC4: 'FINAL Comment on nhess-2022-90', Anonymous Referee #2, 14 Oct 2022
    • AC11: 'Reply on RC4', Edris Alam, 02 Nov 2022
  • RC5: 'Comment on nhess-2022-90', Anonymous Referee #3, 18 Oct 2022
    • AC9: 'Reply on RC5', Edris Alam, 01 Nov 2022
    • AC10: 'Reply on RC5', Edris Alam, 01 Nov 2022

Fahim Sufi et al.

Fahim Sufi et al.

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Short summary
Using the landslide inventory of Chittagong Metropolitan Area (CMA), we created a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054X1041 possible scenario sets and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario.
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