Urban flood disaster warnings have evolved from process warnings based on thresholds of hydrological factors such as water levels and flow rates to response warnings based on impacts and risks of urban flood. This is not only a significant upgrade in disaster management but also a comprehensive innovation in disaster prevention and mitigation concepts and practices.
With the rapid urbanization process, over 60% of the population now resides in cities. Large-scale, high-frequency population movements have become the norm in China. These movements and gatherings increase the exposure and vulnerability of assets in urban flood disasters, thereby increasing flood disaster risks. Incorporating dynamic population activities into flood disaster risk analysis and establishing a precise disaster risk warning system aimed at guiding public travel can effectively reduce urban flood risks and meet the current demands for urban flood emergency response and public safety.
Multisource geographic big data provides unprecedented social perception tools for analyzing spatiotemporal activity patterns of populations, transforming the model of disaster warning and risk management. However, due to the complexity of population movement behaviors, uncovering behavior patterns and changes in travel behavior during flood disasters, and achieving risk measurement through the coupling of flood disaster dynamics and population activity simulation, remains a critical challenge in urban flood warnings.
This project addresses national major needs by focusing on the significant real-time dynamic changes in urban flood disaster risks. Using big data perception technology, it seeks new methods for assessing and warning urban flood disaster risks from the perspective of the interaction between people and the disaster system. The research explores place-based urban flood disaster risk representation, establishes a flood risk simulation model based on the population-place interaction coupling, and proposes urban flood risk warning and response strategies under multidimensional disaster scenarios. This not only provides theoretical methods for flood disaster response and disaster prevention and mitigation in China but also offers scientific decision-making and practical guidance to ensure urban public safety.
National Natural Science Foundation of China: Urban Flood Disaster Risk Simulation and Warning Based on Population-Place Interaction Coupled with Big Data Perception
