Application of the Support Vector Machine on precipitation-runoff modelling in Fenhe River

Abstract

The Support Vector Machine (SVM), a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish rainfall-runoff relationships model. The lags associated with the input variables are determined by applying the hydrological concept of the response time, and a trial-and-error with cross-validation was used to derive the support vector machine (SVM) model parameters. The purpose of this study is to develop a parsimonious model used little observation gage that accurately simulates semi-arid regions by using the SVM model. The rainfall-runoff relations were treated as a non-linear input/output system to simulate the response of runoff to precipitation and applied the model to the upstream of the Fenhe River, the branch of the Yellow River (China), a representative of watershed in a semiarid area. The precipitation-runoff relationships on these regions were studied by using SVM model. Moreover, the SVM model was compared with a previous Artifical neural networks (ANN) model and it was found that the SVM model performed better. Results obtained showed that runoff forecasts of daily time step were better in non-flood season than those made in flood season and monthly runoff forecasts. It suggests that the SVM model and the developed method proposed are convenient and practical for semi-arid regions.