Analysis of Flood Risk Exposure and Insurance Adoption: Data-Driven Insights to Enhance Community Resilience

Flood risk is a growing concern globally, yet there remains a significant gap between high-risk exposure levels and flood insurance penetration. This study explores the application of machine learning techniques to model and analyze factors influencing flood insurance adoption in the continental U.S. By leveraging large-scale datasets that include demographic, socioeconomic, and environmental variables, we aim to identify key determinants that drive or hinder the uptake of flood insurance in high-risk areas.

Our approach employs a variety of machine learning models, including random forests, gradient boosting, and neural networks, to capture complex, non-linear relationships between these factors. By analyzing these models, we identify critical leverage points where interventions could effectively increase insurance penetration, thereby reducing the financial vulnerability of at-risk populations.

Preliminary results indicate that factors such as income level, prior flood experience, and awareness of flood risks are among the most significant predictors of insurance adoption. Moreover, the models reveal regional disparities in these predictors, suggesting that tailored strategies may be necessary to address the unique challenges in different areas.

This research contributes to the growing field of disaster risk management by offering data-driven insights into reducing the flood insurance gap. It ultimately aims to enhance community resilience against flood-related disasters.