Flooding is a recurring event, and Bangladesh is particularly vulnerable to it. This study aimed to assess the Jamuna floodplain's community vulnerability. A concise overview of the literature had performed to identify and finalize the indicators of vulnerability. Expert judgments used to weight the selected indicators. Primary data were collected using a pretested questionnaire throughout the field, where 400 households have been interviewed. Using multi-stage sampling techniques, five Upazilas of Jamalpur district, including Dewanganj, Islampur, Madarganj, Melandaha, and Sharishabari, were purposively chosen following extensive flood damage reports. Percentage value was derived using SPSS for each variable collected from the field survey. The variable vulnerability index (VVI) was derived by dividing the weighting value by the field percentage value for each variable. Exposure, sensitivity, and adaptive capacity have been calculated using the variable vulnerability index values. Finally, using a widely recognized established index, the vulnerability was assessed for the study area. The calculated vulnerability scores for Dewanganj, Islampur, Madarganj, Melandaha and Sharishabari are 0.86, 0.84, 0.71, 0.70 and 0.65. According to the survey findings, all the study sites are found highly vulnerable to floods, especially Dewanganj and Islampur Upazilas, due to poor socio-economic conditions, low adaptive capacities, and high exposures. This study recommends resistant materials like brick and concrete in housing construction, sustainable flood-resistant dams and improving their adaptive capacities through socio-economic uplift.

Integrating hazard, vulnerability, and exposure into a comprehensive assessment of flood risk is critical for sustainable disaster management and building community resilience in coastal urban environments. This presentation synthesizes findings from four investigations to explore the interplay between hazards, vulnerability, and exposure in diverse coastal settings along the U.S. Gulf Coast. First, an analysis of Mobile Bay, Alabama, spanning 2000–2020, illustrates shifting patterns of social vulnerability amidst rapid urbanization and changes in land use and land cover (LULC). Hotspot and cluster analyses identify regions requiring special policy attention to mitigate heightened disaster risks. Second, a similar spatiotemporal analysis of vulnerability in relation to LULC changes in Harris County, Texas during the same period (2000-2020), reveals comparable patterns, highlighting areas where rapid urbanization has amplified vulnerability. Third, a flood risk model for Harris County integrates flood susceptibility mapping using machine learning with a social vulnerability index, exposing discrepancies with the Federal Emergency Management Agency’s (FEMA) 100-year floodplain estimations. Finally, building on insights from the first three studies, a novel conceptual and methodological framework is proposed, integrating flood hazard, social vulnerability, and exposure into flood risk assessment for Tampa Bay, Florida. This framework employs multiple machine learning techniques to provide a more nuanced flood risk evaluation. Collectively, these findings underscore the necessity of integrating social and environmental datasets in flood risk assessments over time to improve resource allocation and foster long-term community resilience.