LSTM Model-Based Rapid Prediction Method of Urban Inundation with Rainfall Time Series

In recent years, with the increasing frequency of extreme rainfall events, the resulting urban inundation disasters have become increasingly severe. Rapid and accurate urban flood simulation and prediction are of great significance for disaster prevention and mitigation. However, physically-based numerical models require substantial computation time for simulating urban flood processes. In this study, we introduce the LSTM algorithm to replace physically-based numerical models for the rapid prediction of flood processes at urban inundation points. First, a hydrological-hydrodynamic numerical model for the study area is constructed to simulate flood processes under different rainfall scenarios, forming a result database. Next, the LSTM algorithm is used to train and learn from the simulated flood data, and the reliability of this learning method is verified. Finally, a rapid prediction model for flood processes at inundation points in the study area is developed. The results indicate that the prediction model achieves high accuracy, with R² values above 0.90 for predicting flood processes and peak flood characteristics at single inundation points. The MAE is no greater than 0.069, and the RMSE is no greater than 0.077. The error in the inundation process ranges between − 0.5% and 0.5%. In terms of efficiency, the average time taken to predict a single rainfall event is only 0.193 s, compared to 4625.92 s for the hydrodynamic model, representing a speedup of approximately 23,968 times relative to the physically-based numerical model. These findings demonstrate that this method meets the needs of daily urban early warning and forecasting work, enhances the city’s disaster prevention and mitigation capabilities, and effectively reduces the loss of life and property.