Coastal communities worldwide face increasing flood risks due to climate change. Water level sensors offer real-time flood risk information, but optimal sensor placement remains challenging. This study presents a multi-objective optimization approach for strategically locating sensors, incorporating traditional network performance measures (coverage, uncertainty) and flood-specific parameters (hazard estimations, infrastructure exposure, serviceability, social vulnerability). A workflow combining quantitative analysis with local expertise is proposed. The method effectively reduces potential sensor locations, supporting community decision-making and enabling sequential network expansion for hyperlocal flood risk assessment and mitigation.

Iris Tien, Jorge-Mario Lozano, Akhil Chavan