Long-term runoff forecasting based on SVR model and its uncertainty analysis

In accordance with the Huangzhuang Station's monthly runoff from 1981 to 2008 and the correlativity from 1980 to 2007 among the 74 circulation indexes of each month, the monthly north pacific sea surface temperature field, and the 500hPa geopotential height, we used the stepwise regression method to select the forecast factors and built a GA-SVR Model (Genetic Algorithm Support Vector Regression Model) on the basis of GA ( Genetic Algorithm) , inorder to forecast the monthly runoff from 2009 to 2013. The results showed that the accuracy of the runoff forecast was relatively high: the average relative error in flood season was within 25% ; the yearly runoff amount was within 20% in non-flood season. It was superior to Random Forest and Multiple Regression Model. With the forecast results of the GA-SVR Model as the basis of the probability forecast, we used the Hydrologic Uncertainty Processor (HUP) of the Bayesian Theory to analyze the forecast reliability. The outcome indicated that HUP could not only give a constant-value forecast with relatively high accuracy , but also quantify the forecast reliability in the form of a confidence interval to provide more forecast information.

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