A Machine Learning Framework for Assessing Systemic Societal Vulnerability to Coastal Flooding

Vulnerability indices are increasingly used to understand why coastal urban communities experience differential flood impacts. However, they are criticised for subjective indicator selection, weighting and aggregation, which rely on researcher judgement and rarely integrate uncertainty. This study develops a machine‐learning framework for comparing and ranking household‐level vulnerability to coastal flooding in the Lekki Peninsula, Nigeria. The research employed a multi‐source dataset, including household surveys, key informant interviews, field observations and secondary data. The expectation‐maximisation (EM) algorithm identifies latent vulnerability clusters, while support vector machines (SVMs) model nonlinear relationships to predict household membership. A probability‐weighted index and confidence threshold are integrated to quantify classification reliability. The EM‐SVM model achieved 98% accuracy on the test dataset, with near‐perfect separability, as indicated by an area under the curve of 0.99. Spatial analysis showed that 7.4% of households were highly vulnerable, 68.9% moderately vulnerable and 23.5% had low vulnerability. Five vulnerability profiles emerged; two exhibited relatively high education and, in one case, higher income, but remained moderately vulnerable due to dependence on flood‐exposed healthcare and water infrastructure and housing types susceptible to flooding. One profile showed low vulnerability due to lower exposure of healthcare infrastructure and higher self‐perceived coping capacity, indicating the role of critical infrastructure reliability and psychological preparedness. The findings reinforce the understanding that vulnerability is systemic and intersectional rather than determined by independent socio‐economic attributes. Through this framework, the vulnerability approach reduces bias and offers a transparent, data‐driven alternative to conventional approaches, thereby reinforcing the evidence base for equitable flood‐risk planning and more targeted adaptation interventions.