Fast Simulation of Urban Waterlogging Based on Multi-Objective Machine Learning Model

The frequent occurrence of urban waterlogging disasters induced by rainstorm has recently caused serious economic losses and casualties in China. Numerical simulation of waterlogging is an important tool for disaster prewarning and forecasting as well as disaster prevention and control; however, the traditional numerical physical models have the disadvantage of low computational efficiency, which makes it difficult to meet the demand for real-time simulation and real-time early warning and forecast. To this end, this study combines the respective advantages of coupled rainstorm-flood models with physical mechanisms and machine learning algorithms, and proposes a rapid prediction and simulation method for inundated depth of urban waterlogging based on multi-objective machine learning algorithms. The forecasting performances of K-Nearest Neighbors(KNN), Multi-Objective Random Forest(MORF), Extreme Gradient Boosting(XGBoost) and their integrated models are discussed, respectively. The results show that: (1)The coupled rainstorm- flood model based on SWMM and LISFLOOD-FP has good applicability in the simulation of urban waterlogging induced by rainstorm in the study area. On this basis, the database with a total of 70 scenarios of rainstorm-inundation with different characteristics were generated. (2)The KNN, MORF, XGBoost and their integrated models all have good results in predicting water depth, with Pearson correlation coefficient (PCC)values all above 0. 812, mean absolute error(MAE)below 6. 9 cm, and root-mean-square error (RMSE)less than 0. 116. The KNN-MORF-XGBoost integrated model has the best overall results, with the average values of MAE, PCC and RMSE reaching 2. 4cm, 0. 965 and 0. 043, respectively. (3)In addition to the high prediction accuracy, the prediction speed of the constructed multi-objective machine learning prediction model is extremely fast, and the water depth simulation efficiency is more than 20 times higher than that of the coupled rainstorm-flood model. This study can provide a reference for the application of machine learning in the rapid simulation of urban waterlogging induced by rainstorm, which is of great value for the early warning and forecast of urban waterlogging disaster.