Affected by many factors, the hydrological time series is non-stationary. Traditional models of time series, such as ARAM, require data to be stable and not suitable for forecasting hydrological time series. In recent years, machine learning algorithms are increasingly used to study hydrological processes. In this paper, Support Vector Regression (SVR) and Bayesian Ridge Regression (BRR) are applied to the prediction of monthly precipitation. Wavelet transform is used to decompose and reconstruct precipitation data, and then phase space reconstruction is carried out for each sub-sequence. The model with higher precision on each sub-sequence is selected from SVR and BRR with verification data, so as to construct the BRR-SVR optimization model and compare it with BRR and SVR models. Taking Beijing Station, Nanjing Station and seven rain stations in Taihu River Basin as examples, the prediction performance of each model is evaluated by coefficient of determination, mean absolute percentage error and mean absolute error, and the relative error graph is used to reveal the differences among the three models. The calculation results verify the validity of the optimization model for Nanjing Station and Taihu River Basin. © 2019, China Water Power Press. All right reserved.
