Numerical simulation models are commonly used to analyze and simulate urban waterlogging risk. However, the computational efficiency of numerical models is too low to meet the requirements of urban emergency management. In this study, a new method was established by combining a long short-term memory neural network model with a numerical model, which can quickly predict the waterlogging depth of a city. First, a numerical model was used to simulate and calculate the ponding depth of each ponding point under different rainfall schemes. Using the simulation results as training samples, the long short-term memory neural network was trained to predict and simulate the waterlogging process. The results showed that the proposed “double model” prediction model appropriately reflected the relationship between the changes in waterlogging depth and the temporal and spatial changes in rainfall, and the accuracy and speed of computation were higher than those of the numerical model alone. The simulation speed of the “double model” was 324,000 times that of the numerical model alone. The proposed “double model” method provides a new idea for the application of artificial intelligence technology in the field of disaster prevention and reduction.

Due to the existence of drainage networks, urban areas have formed their own hydrological mechanism. The pretreatment of complex and elaborate drainage network data has become a challenging step in building an urban hydrological model. This study proposes a network-combing method based on the potential outfall mechanism for an urban drainage system, analyzes the topological structure of the underground network, and generates a subcatchment based on the potential outfall (SBPO). Two hydrological methods are constructed for a typical region in Kunming, Yunnan Province, China. The results show that: The network-combing method of potential outfall mechanisms can well complete the sorting work of a drainage network system and can clarify the relative independent relationship. The SBPO method basically agrees with the SWMM constructed with a high-resolution network in terms of runoff volume, the peak value and the duration of the outflow process at the outfall. However, the subcatchment by the potential outfall mechanism can help to understand the service partition, and the calculation cost is greatly reduced. The method emphasizes the importance of the influence of a drainage system on water confluence, which can help to better understand the process of runoff in urban areas.