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.

A 2D hydrodynamic model was established based on MIKE Flood, which could simulate the flooding submergence from a burst levee. The flood risks in the urban area were quantitatively evaluated for several scenarios including burst levee, proactive flood diversion and their combinations. The results could provide scientific supports for urban flood risk analysis, flood dispatching and management. The methodology has been applied in Qiqihar, which is one of the main cities in northeast China.

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.