Dynamic Assessment of Flood Hazard Based on Points-of-Interest Data

Abstract

In order to better formulate flood prevention and disaster mitigation measures and reduce the impact of urban flood on social development, it is necessary to conduct a scientific and accurate flood hazard assessment. The development of big data technology has brought new opportunities for flood hazard assessment. This study used a coupling model to simulate urban flood, and used the HR method to classify flood hazard. The dynamic change process of two types of Points-of-Interest(POIs) for catering services and living services under different flood hazard degrees were counted. The results showed that (1) In the northern part of the basin, mountainous, impervious ground and the lack of effective drainage channels have combined to cause more serious floods; (2) The flood hazard were mainly low-degree in the study area. Moderate flood hazard mainly occurred in farmland and built-up land. High-degree and extreme-degree flood hazard mainly occurred on both sides of rivers in the northwest of the basin; (3) Affected by the rainfall pattern, the number of POI affected by flood presented the characteristics of “three stages” under four flood hazard degrees; (4) The POIs of the two services was most seriously affected by the flood when the rainfall just stopped; (5) In terms of the proportion of POIs affected by floods, the impact of floods on the two industries is basically the same, but from the perspective of the number of POIs affected by floods, catering services will be more affected; (6) The spatial location of the POIs led to a slight difference in the trend of the number of POIs under high-degree and extreme-degree flood hazard. This study provided a new method for urban flood hazard dynamic assessment, which could help decision makers formulate more targeted flood prevention and disaster mitigation measures