Optimizing Flood Prevention Infrastructure Placement Using a Genetic Algorithm-Based Framework

Flooding presents a critical threat to infrastructure and communities in Southeast Texas, demanding innovative and adaptive solutions for mitigation. This study used genetic algorithms (GA) to identify optimal designs for flood prevention infrastructure, including levees and detention ponds. Departing from traditional approaches, GA enables the exploration of placement configurations that minimize flood damage while maintaining cost-effectiveness. Using a geospatial environment informed by Southeast Texas’s terrain and hydrological data, the algorithm iteratively determines infrastructure placement. The objective function balances flood reduction with budget constraints. Results demonstrate that the GA-based approach outperforms conventional methods, offering a robust and scalable framework for flood resilience planning. This research underscores the efficacy of GA as a data-driven tool to enhance decision-making in vulnerable regions, contributing to the broader goal of resilient flood infrastructure.