The application of big data in the analysis of the impact of urban floods: A case study of Qianshan River Basin

In recent years, with the acceleration of climate change and urban modernization, the flood risk of economic and social development has increased. The problem of urban flood disasters needs to be solved urgently. It is particularly important to conduct flood disaster loss assessment research for further flood control and disaster reduction, and emerging big data have opened a new direction for solving the problem of urban flooding. In this study, the Qianshan River Basin in Guangdong Province was used as an example. The TELEMAC model was used to simulate the inundation process under the design rainstorms of 50a and 100a in the Qianshan River Basin. Based on the heat map service, route planning service, and search service functions of Baidu Maps and Amap open platforms, this study used crawler technology to integrate multi-source data, including traffic survey data, geospatial data, population heat map data at different times (day/night) on weekdays and non-weekdays, and point of interest (POI) data from various industries. A dynamic assessment model of flood disaster loss for traffic, population, and POIs was constructed. The research results could greatly improve the timeliness and accuracy of urban flood disaster analysis, early warning, monitoring, and disaster risk assessment.