PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913562026311680 |
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| author | Mandal, Debayan Zou, Lei Wilkho, Rohan Singh Abedin, Joynal Zhou, Bing Cai, Heng Baig, Furqan Gharaibeh, Nasir Lam, Nina |
| author_facet | Mandal, Debayan Zou, Lei Wilkho, Rohan Singh Abedin, Joynal Zhou, Bing Cai, Heng Baig, Furqan Gharaibeh, Nasir Lam, Nina |
| contents | In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. Meanwhile, there is a lack of computationally rigorous, user-friendly tools that can support customized resilience assessment considering local conditions. This study aims to address these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customized Resilience Inference Measurement designed for multi-scale community resilience assessment and influential socioeconomic factors identification, 2) To implement a Platform for Resilience Inference Measurement and Enhancement module in the CyberGISX platform backed by high-performance computing, 3) To demonstrate the utility of PRIME through a representative study. CRIM generates vulnerability, adaptability, and overall resilience scores derived from empirical hazard parameters. Computationally intensive Machine Learning methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement Mandal, Debayan Zou, Lei Wilkho, Rohan Singh Abedin, Joynal Zhou, Bing Cai, Heng Baig, Furqan Gharaibeh, Nasir Lam, Nina Machine Learning In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. Meanwhile, there is a lack of computationally rigorous, user-friendly tools that can support customized resilience assessment considering local conditions. This study aims to address these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customized Resilience Inference Measurement designed for multi-scale community resilience assessment and influential socioeconomic factors identification, 2) To implement a Platform for Resilience Inference Measurement and Enhancement module in the CyberGISX platform backed by high-performance computing, 3) To demonstrate the utility of PRIME through a representative study. CRIM generates vulnerability, adaptability, and overall resilience scores derived from empirical hazard parameters. Computationally intensive Machine Learning methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. |
| title | PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.09463 |