Machine Learning-based Prediction of Maximum Inundation Depth and Maximum Inundation Flow Rate for Flooding

In this paper, taking Yangshuo County, which is frequently affected by mountain floods and Lijiang River transit floods together, as an example, we utilize the high-precision flood data generated by the coupled hydrological-hydrodynamic models in a machine learning sample library and construct the maximum inundation water depth prediction model (KNN-Depth) and the maximum inundation flow rate prediction model (KNN-Speed) based on the machine learning K-nearest-neighbor algorithm (KNN) to predict the maximum inundation water depth and maximum inundation flow rate. The prediction performance of the models has been tested and examined with 17 data sets of historical floods, in which a total of six flood risk classes have been classified to carry out flood risk prediction in the study area by considering the two indicators of maximum inundation water depth and maximum inundation flow rate. The results show that: 1) The maximum inundation water depth predicted by KNN-Depth is close to the simulation results of the maximum inundation water depth of the hydrodynamic model, and the mean values of the Coefficients of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) indexes of the 17 floods are 0.988, 0.127m, and 0.060m, respectively; 2) The maximum inundation flow velocity generated by KNN-Speed model is similar to the simulation results of the maximum inundation flow velocity of the hydrodynamic model, and the mean values of R², RMSE and MAE indicators of the 17 floods are 0.968, 0.051m/s and 0.020m/s, respectively; 3) The running time of KNN-Depth and KNN-Speed models is 2s and 10s, respectively for the prediction; 4) The research indicates that the flood risk in Yangshuo County within 500m of the banks of the main rivers during a large flood is level 6 in general, and the flood risk in the new and old urban areas is up to level 5.