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Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF)
Communities are ever-evolving, cities are constantly expanding, and the threat of natural hazards has escalated like never before. Cities can develop and prosper only if their society is resilient to external shocks. Measuring community resilience over time is crucial with the influence of technolog...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906364/ https://www.ncbi.nlm.nih.gov/pubmed/35287382 http://dx.doi.org/10.1007/s11069-022-05299-7 |
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author | Ali, Sameer George, Abraham |
author_facet | Ali, Sameer George, Abraham |
author_sort | Ali, Sameer |
collection | PubMed |
description | Communities are ever-evolving, cities are constantly expanding, and the threat of natural hazards has escalated like never before. Cities can develop and prosper only if their society is resilient to external shocks. Measuring community resilience over time is crucial with the influence of technology and change in community lifestyles. With the frequent onset of floods in Kerala in recent years, the community must be well-prepared for future calamities. Thus, this paper develops a community resilience index for Kerala’s urban flood-prone areas (CRIF) through a rigorous bottom-up approach. The criteria for the index were developed using multi-criteria decision analysis that covered a fuzzy Delphi study, an empirical study using multi-variate probit regression, and an AHP analysis. The fuzzy Delphi study selected seven criteria: ‘social’, ‘economical’, ‘governance/political’, ‘health’, ‘communication/coordination, ‘education’, and ‘infrastructure’ from 65 experts. The empirical study helped apprehend the public’s viewpoints under each criterion. Finally, the AHP analysis helped assign appropriate weights to the criteria which 28 experts designated. The index is also designed according to the Sendai Framework for Disaster Risk Reduction (2015–2030). Further, the CRIF Index is put into action through a case study of the Kochi Municipal Corporation area, and the results are also validated using the Spearman’s rank correlation coefficient method. Results from validation returned a value of 0.7209 for the perceived CRIF method and 0.5798 for the external validation method, which corresponds to a ‘high’ and ‘moderate’ correlation, respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11069-022-05299-7. |
format | Online Article Text |
id | pubmed-8906364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-89063642022-03-10 Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) Ali, Sameer George, Abraham Nat Hazards (Dordr) Original Paper Communities are ever-evolving, cities are constantly expanding, and the threat of natural hazards has escalated like never before. Cities can develop and prosper only if their society is resilient to external shocks. Measuring community resilience over time is crucial with the influence of technology and change in community lifestyles. With the frequent onset of floods in Kerala in recent years, the community must be well-prepared for future calamities. Thus, this paper develops a community resilience index for Kerala’s urban flood-prone areas (CRIF) through a rigorous bottom-up approach. The criteria for the index were developed using multi-criteria decision analysis that covered a fuzzy Delphi study, an empirical study using multi-variate probit regression, and an AHP analysis. The fuzzy Delphi study selected seven criteria: ‘social’, ‘economical’, ‘governance/political’, ‘health’, ‘communication/coordination, ‘education’, and ‘infrastructure’ from 65 experts. The empirical study helped apprehend the public’s viewpoints under each criterion. Finally, the AHP analysis helped assign appropriate weights to the criteria which 28 experts designated. The index is also designed according to the Sendai Framework for Disaster Risk Reduction (2015–2030). Further, the CRIF Index is put into action through a case study of the Kochi Municipal Corporation area, and the results are also validated using the Spearman’s rank correlation coefficient method. Results from validation returned a value of 0.7209 for the perceived CRIF method and 0.5798 for the external validation method, which corresponds to a ‘high’ and ‘moderate’ correlation, respectively. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11069-022-05299-7. Springer Netherlands 2022-03-09 2022 /pmc/articles/PMC8906364/ /pubmed/35287382 http://dx.doi.org/10.1007/s11069-022-05299-7 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper Ali, Sameer George, Abraham Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title | Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title_full | Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title_fullStr | Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title_full_unstemmed | Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title_short | Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF) |
title_sort | modelling a community resilience index for urban flood-prone areas of kerala, india (crif) |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906364/ https://www.ncbi.nlm.nih.gov/pubmed/35287382 http://dx.doi.org/10.1007/s11069-022-05299-7 |
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