Adaptive selection and optimal combination scheme of candidate models for real-time integrated prediction of urban flood

Abstract

The ability to predict urban floods is crucial for reducing potential losses. Previous studies suggest that a multimodel combination is an effective way to improve the prediction performance of urban flood models; however, few studies have systematically investigated the impact of candidate models on the performance of the integrated model. Therefore, this study proposes a multimodel integrated forecasting method for urban flooding from the perspective of the response relationship between the candidate models and integrated model. The results of this study suggest that the prediction error of the proposed was reduced by 46.9%–64.6% compared with that of the single model. The results of various candidate model combinations indicate that there is a threshold effect for the number of candidate models in the integrated model; the integrated model with six candidate models exhibited the highest prediction accuracy. However, the increase in the number of candidate models was accompanied by a significant decrease in computational efficiency of the integrated model. Based on the accuracy and timeliness requirements of urban flood prediction, a scheme combining gradient lifting decision tree, random forest, back propagation, and support vector machine models was found to be the best candidate model combination scheme. The real-time warning results of the aforementioned combination model provided superior warning performance. The results of this study provide a reference for the construction of more suitable urban flood models, real-time forecasting, and warnings.