Revealing spatial inequalities in flood exposure across São Paulo State, Brazil

Abstract

Floods remain among the most frequent and destructive natural hazards, requiring spatially explicit tools to support risk-reduction planning and emergency preparedness. This study develops an integrated flood susceptibility and exposure assessment for São Paulo State, Brazil, combining machine learning, explainable artificial intelligence, and census-based demographic information. Flood Susceptibility Maps (FSMs) were generated at 30 m spatial resolution using the Random Forest (RF) algorithm, while the contribution of each conditioning factor was interpreted using SHapley Additive exPlanations (SHAP). Human exposure was assessed by integrating the susceptibility map with the spatial granularity of the 2022 Brazilian Institute of Geography and Statistics (IBGE) Census. Based on 15 conditioning factors and a balanced dataset of 2, 000 flood-prone and 2, 000 non-flood-prone samples, the RF model achieved high predictive performance (accuracy = 0.939; AUC = 0.982), with river proximity, slope, Curve Number, and altitude emerging as the most influential conditioning factors. Integrating susceptibility patterns with census data revealed marked spatial inequalities in flood exposure: the 6.2% of the state territory classified as Very High flood susceptibility concentrated 22.3% of the population, corresponding to approximately 9.89 million people, and 23.5% of all housing units. The municipal-level analysis further showed that 24.6% of people younger than 17 years and 26.8% of people older than 65 years were located in municipalities classified as High exposure. To support exposure-informed planning, a Combined Prioritization Index (CPI) was developed by integrating dominant municipal susceptibility, relative population exposure, and absolute population exposure. The CPI identified higher-priority municipalities not only along the coastal region but also in inland areas, including Guaruj, Rio Claro, Santos, Itanham, Praia Grande, Cubatão, São Vicente, and Caraguatatuba. Overall, the findings reveal spatial inequalities in flood exposure by showing where elevated flood susceptibility overlaps with concentrated human exposure, providing evidence for more transparent and anticipatory flood-risk planning.