PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement

Author(s): Debayan Mandal, Dr. Heng Cai, Rohan Singh Wilkho, Joynal Abedin, Bing Zhou, Dr. Furqan Baig, Dr. Nasir Gharaibeh, Dr. Nina Lam

Resilience assessment and improvement have become increasingly important in today's world, where natural and man-made disasters are becoming more frequent and severe. Cities, communities, and organizations are recognizing the need to prepare for and mitigate the impacts of disasters and disruptions, and there is a growing body of research and practice on resilience assessment and improvement. One significant research gap in this area is the lack of a customizable platform for resilience assessment and improvement. While there are many tools and frameworks available for assessing and improving resilience, they are often limited in their scope, proprietorship and applicability. Many of these tools are designed for specific types of hazards and may not be easily adapted. A customizable platform for resilience assessment and improvement would address these limitations by providing a flexible and adaptable framework that can be tailored to the needs and priorities of different users. This would enable users to address specific challenges and opportunities in their context, and to leverage existing resources and knowledge to support resilience. Therefore, such a platform would help to build more resilient communities, organizations, and systems, and contribute to a more sustainable and resilient future for all.

Keywords: disaster, Machine Learning, resilience index

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Debayan Mandal

Texas A & M University

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