Multidimensional flood vulnerability assessment in rural communities: an indicator-based fuzzy cognitive mapping framework

Purpose This study aims to assess multidimensional flood vulnerability in rural communities by integrating statistical, spatial and fuzzy cognitive mapping (FCM) approaches. It identifies how economic, social, environmental and infrastructural factors interact to shape resilience and highlights key leverage points for targeted flood risk reduction. Design/methodology/approach A descriptive–analytical survey was conducted across 32 flood-prone villages in East Alamut, Iran. Data from 52 validated indicators were analyzed using one-sample t-tests, ANOVA, Scheffé post-hoc tests, GIS spatial interpolation and FCM network modeling. Sensitivity and degree analyses were applied to classify indicators as influential, balanced or dependent. Findings Overall vulnerability was significantly above the reference level (mean = 3.42, p < 0.001), with the social (3.64) and economic (3.58) dimensions most critical. Spatial heterogeneity was evident, with certain villages showing extreme vulnerability. FCM identified income, employment, health, social cohesion, natural resource degradation and infrastructure disruption as highly sensitive indicators. Results emphasize that single-sector interventions are ineffective, and integrated multidimensional strategies yield stronger resilience outcomes. Research limitations/implications The framework guides policymakers in prioritizing interventions that jointly enhance livelihoods, infrastructure and social resilience in flood-prone rural areas. Practical implications The proposed framework provides policymakers and practitioners with a practical decision-support tool for prioritizing interventions in flood-prone rural areas. By integrating statistical assessment, spatial analysis and FCM, it identifies critical leverage points where limited resources can achieve maximum resilience impact. The approach helps planners design integrated programs that simultaneously strengthen livelihoods, infrastructure and environmental stability. It supports evidence-based resource allocation, improves risk communication and facilitates cross-sectoral coordination among governmental, local and community actors, thereby enhancing the effectiveness and sustainability of disaster risk reduction strategies. Social implications This study demonstrates that social factors – such as community cohesion, health awareness, education and local participation – are pivotal in shaping rural resilience to floods. It highlights that vulnerability extends beyond physical exposure and is strongly influenced by psychosocial conditions, institutional trust and risk literacy. Strengthening social networks, enhancing disaster education and supporting vulnerable groups can substantially reduce systemic risks. The results emphasize that social resilience must be central to flood management policies, as communities with strong social capital recover faster, adapt better and sustain resilience in the face of increasing hydro-meteorological hazards. Originality/value This study introduces a novel indicator-based FCM framework combining statistical and cognitive-network modeling for flood vulnerability assessment. It advances understanding of causal linkages among vulnerability dimensions and provides a transferable tool for spatially targeted, evidence-based resilience planning.