Research on an urban flood early warning model based on multi-source data collaborative perception.

Urban waterlogging presents a significant menace to urban operations and the livelihoods of residents. It is of the utmost necessity to establish an accurate and efficient early warning system. This research focuses on multi-source data fusion and intelligent models, and conducts a comprehensive exploration of the integration of data from meteorology, hydrology, geospatial information, and drainage systems. It processes multi-source data in real time through a distributed computing architecture. By applying methods such as the Horton infiltration formula, the isochron method, the Saint-Venant equations, and the Hazen-Williams formula, precise simulation of surface runoff and monitoring of urban drainage capacity are realized. Furthermore, the waterlogging risk level is dynamically adjusted according to real-time data. The experimental findings suggest that, when compared with AquaTalk, MIKE FLOOD, CAE S.p.A., and FIEDLER, the urban waterlogging early warning model proposed in this paper shows improvements in the accuracy, reliability, timeliness, and spatial precision of early warning. This offers a reference for urban waterlogging prevention and disaster relief.