Urban flash flood forecast using support vector machine and numerical simulation

Abstract

Abstract In order to provide urban flood early warning effectively, two support vector machine (SVM) models, using a numerical model as data producer, were developed to forecast the flood alert and the maximum flood depth, respectively. An application in the urban area of Jinlong River Basin, Hangzhou, China, showed the superiority of the proposed models. Statistical results based on the comparison between the results from SVM models and numerical model, proved that the SVM models could provide accurate forecasts for estimating the urban flood. For all the rainfall events tested with an identical desktop, the SVM models only took 2.1 milliseconds while the numerical model took 25 hours. Therefore, the SVM model demonstrates its potential as a valuable tool to improve emergency responses to alleviate the loss of lives and property due to urban flood.