Urban flood hazard insights from multiple perspectives based on internet of things sensor data

Abstract

Floods are major global natural disasters that cause significant damage. Analyzing urban flood hazards is essential for urban planning and sustainable development. The shift toward proactive urban disaster prevention requires expanding flood hazard assessments beyond flood depth to encompass a broader range of factors to enhance resilience. This study introduces a multiple perspective analysis of urban flood hazards based on internet of things (IoT) sensor data, such as maximum flood depth, total flood events, average drainage time, average accumulation efficiency and average drainage efficiency. This research assesses detailed flood hazards of urban areas and points of interest (POIs) and finds a significant difference of up to 14.6 % in extreme-hazard areas when multiple hazard indicators are used, with the maximum flood depth indicator showing the highest proportion. For medium-hazard areas, the total flood event indicator yielded the highest proportion, accounting for up to 35.7 % of the area. The findings also indicate that POI flood hazards vary significantly depending on the indicator. Medical facilities were found to have extended impacts due to prolonged water accumulation and drainage times, despite infrequent flooding, suggesting that many locations are subject to a moderate hazard level. The study also highlights the heightened hazard of residential buildings in extreme scenarios, underscoring the need for enhanced flood mitigation in residential planning. This study emphasizes adopting multiple perspectives in flood hazard assessment, challenging the traditional reliance on single metrics. This study provides valuable insights for urban planners and policy-makers and advocates for a holistic approach to urban flood risk.