Under the background of rapid urban development and continuous climate change, frequent floods around the world have caused serious economic losses and social problems, which has become the main reason for the sustainable development of cities. Flood disaster risk assessment is an important non-engineering measure in urban disaster prevention and mitigation, and scientific flood disaster risk assessment is the premise and foundation of flood disaster risk management. This paper summarizes the current situation of flood risk assessment by analyzing the international literature in recent 20 years. The mechanism of flood disaster is mainly discussed. The flood disaster assessment methods are summarized, including historical disaster statistics method, multi-criteria index system method, remote sensing and GIS (Geographic Information System) coupling method, scenario simulation evaluation method and machine learning method. Furthermore, the development status of flood risk analysis and forecasting is summarized. Finally, the development trend and direction of flood risk assessment are put forward.

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters.

Identifying urban functional zones is an important task in urban planning and smart city construction. Accurately identifying urban functional zones and analyzing their spatial distribution is crucial for optimizing urban spatial layout, improving urban management and balancing human–environment interaction. However, most of the existing studies focus on the analysis of individual data sources, which have difficulty fully reflecting the complex spatial structures and functional differences in cities. To solve this problem, this paper proposes a new method of urban functional area identification which integrates multi-source data and advanced algorithms. By clustering the results of multi-factor weighted kernel density, the paper can more accurately quantify the spatial distribution characteristics of urban functional areas and better reflect the functional differences among urban areas. At the same time, this paper uses the Random Forest model to optimize the POI data and the building data to improve the classification accuracy and the generalization ability of the model. The results show that the distribution of functional areas in the Fifth Ring Road region of Beijing presents the characteristics of diversification and agglomeration: the core urban area is dominated by high-density commercial service and public service functional areas, with a high degree of functional integration; The peripheral areas are mainly residential areas and green areas, with dispersed distributions, but clear functions. The overall accuracy reaches 87%, and the model can effectively reflect the distribution and spatial characteristics of urban functional areas.