Multi-index model for assessing population vulnerability and risk profiles after cyclone Amphan: a case study of Satkhira district

Tasnim Zarin Meem (), Emon Barua () and Md. Najmul Kabir ()
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Tasnim Zarin Meem: Shahjalal University of Science and Technology
Emon Barua: Shahjalal University of Science and Technology
Md. Najmul Kabir: Shahjalal University of Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 6, No 50, 7584 pages

Abstract: Abstract Tropical cyclones often result in widespread destruction, particularly in vulnerable areas such as Bangladesh. The study focuses on vulnerable coastal communities in Satkhira, Bangladesh, towards tropical cyclones, particularly Cyclone Amphan, applying the multi-model approach to assess the risk, using six major domains: likely social conditions (SC), livelihood strategies (LS), knowledge level, preparedness level, activities during (DD) and after disaster (AD). A probability sampling method was used to select the number of respondents with a 95% confidence interval and 5% margin of error to ensure a sufficient number of representatives, and the sample size was 278. The analysis was integrated through Mean-models, explanatory factor analysis (EFA), confirmatory factor analysis (CFA), and principal component analysis (PCA) to evaluate and validate the vulnerability patterns. The results bring out that SC (mean = 0.63) and LS (mean = 0.66), along with AD (mean = 0.88) domains, show significant vulnerabilities in the study area. EFA highlighted key interactions between socio-economic factors, CFA validated these patterns with robust fit indices (CFI = 0.979, RMSE = 0.029), and PCA effectively reduced the dataset dimension; the first nine principal components can describe 68% of the variance. The mean model demonstrated the best-fit model with R2 = 0.54, though every model depicts unique insights from the variables to observe the vulnerabilities. Key findings reveal the role of infrastructure deficiency, economic instability, and inadequate preparedness to mitigate the impact of disasters, exacerbating the risk in Satkhira. This research advances the understanding of vulnerability through methodological exactness. It combines statistical modelling with qualitative perceptions and highlights the importance of economic resilience, infrastructure development, and preparedness strategies. The study emphasizes its societal values in informing disaster risk reduction policies, reducing inequalities, and fostering sustainable recovery.

Keywords: Tropical cyclones; Coastal communities; Statistical modelling; Livelihood vulnerability index (LVI); Disaster resilience (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-07099-7

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