With global warming and urbanization accelerating, urban flooding is becoming more severe. Real-time forecasting plays a key role in disaster mitigation, but traditional hydrodynamic models are too resource-intensive for timely prediction. Machine learning models offer high efficiency but often lack accuracy in simulating spatiotemporal flood dynamics. This study proposes a new data-driven model, which performs well in a flood-prone area of Macao.

With the rapid development of cities in China, urban flooding becomes a growing problem, which not only affect the living quality of the public, but also threatens urban safety. Recently, urban flooding is attached more attention. By analyzing and summarizing urban flooding data from recent decades, which includes frequency, scope of influence, and losses, the causes and status quo of urban flooding problems in China is generalized.

In this research, the Mike urban in the mountain city was successfully constructed. The simulated results show that the water logging pressure in typical mountain part and the old town is greater than that of the plain part and the new area. The related analysis provides technical support for the early warning of urban waterlogging and construction of drainage infrastructures of study area, and gives references for the simulation of surface runoff process in mountainous cities.