Abstract
Rapid prediction of rainstorm waterlogging is crucial for disaster prevention and reduction. However, the traditional numerical models for simulating and predicting large-scale and complex subsurface conditions are complicated and time-consuming; moreover, the time-efficiency requirement of rainstorm waterlogging prediction is difficult to meet. To address these shortages of the numerical models, this study constructs a spatiotemporal prediction model of urban rainstorm waterlogging based on machine learning methods to rapidly predict waterlogging extent and water depth changes.
This study constructs a rapid prediction model of urban rainstorm waterlogging based on a hydrodynamics model and machine learning algorithms. First, a hydrodynamic model is constructed based on InfoWorks integrated catchment management (InfoWorks ICM) for rainstorm waterlogging in the study area with the parameter rate determination and model validation to realize the high-precision simulation of urban rainstorm waterlogging. On this basis, a rainfall scenario-driven hydraulics model is designed to further obtain rainstorm waterlogging simulation results. These results are used as the base dataset for machine learning. Second, the spatial characteristics data of rainstorm waterlogging are obtained from three aspects: rainfall situation, subsurface information, and the drainage capacity of the pipe network, which, together with the grid simulation results, comprise the dataset. The spatial prediction models are based on random forest, extreme gradient boosting (XGBoost), and K-nearest neighbor algorithms. Finally, the simulation results of waterlogging points are used to generate rainstorm waterlogging time series data. The rainfall, cumulative rainfall, and water depth of the first four moments (every 5 min) are used as the input for a long short-term memory (LSTM) neural network to predict the present water depth of the flooding point. The two models collaborate to achieve rapid spatial and temporal predictions of urban rainstorm waterlogging.
For spatial predictions, the random forest model has the best fitting performance regarding evaluation indexes such as the mean square error, the mean absolute error, and the coefficient of determination (R2). When a rainstorm scenario with an 80-year event and a 2.5 h rainfall calendar prediction set is used, the prediction results concur with the risk map of urban waterlogging in Beijing. Compared with the simulation results of InfoWorks ICM, the prediction accuracy of the predicted inundation extent reaches 99.51%, and the average prediction error of waterlogging depth does not exceed 5.00% by the random forest model. For temporal predictions, the trend of the water depth change of the LSTM neural network model is more consistent with the simulation results of InfoWorks ICM, the R2 of four typical inundation points are above 0.900, the average absolute error of water depth prediction at the peak moment is 1.9 cm, and the average relative error is 4.0%.
When addressing sudden rainstorms, the rapid prediction model based on machine learning algorithms built in this study can generate accurate prediction results of flooding extent and water depth in seconds by simply updating the forecast rainfall data in the model input. The model computational speed is greatly improved compared to the hydrodynamics-based numerical model, which can help plan waterlogging mitigation and relief measures.
