Prediction of hydrological series based on wavelet transform and support vector machine

Support vector machines can not accurately predict time series related to hydrological processes due to the different time series in the data.This study combines wavelet transforms with a support vector machine to predict hydrological time series.The wavelet transform is used to decompose the hydrological time series into subseries with different time scales.Thus,the support vector machine(SVM) is used to simulate and predict the future behavior of each subseries.The results from the SVM are then reconstructed using the inverse wavelet transform.The wavelet-SVM model was used to analyze monthly natural runoff rates at the Sanmenxia hydrological station.The prediction accuracy and the time variations of the model are both excellent,better than the SVM model or an artificial neural network model.This wavelet-SVM model is shown to be universally useful in a range of applications.