River basin flood prediction using support vector machines

This paper presents a river flood prediction technique using support vector machine (SVM). We investigated the 2-year data covering 2005 and 2006 and 7 crucial river floods that occurred in the downtown of Chiang Mai, Thailand. Past and current river levels of the 3 gauging stations are utilized as the input data of the SVM models to predict the river levels at the downtown station in 1 hour and 7 hours in advance. The performances of the SVM models are compared with that of the multilayer perceptrons (MLP) models. The experimental results show that the SVM models can perform better than the MLP models. Moreover, the results from the blind test sets demonstrate that the SVM models are appropriate for warning people before flood events. The proposed SVM prediction models are also implemented in a real-world flood warning system. The predicted river levels are accessible to public via a Web site, electronic billboards, and warning stations all over the city.