In order to effectively improve the accuracy of runoff forecasting, this paper proposed an effective hybrid optimization strategy which was based on the combination of particle swarm optimization and simulated annealing algorithm, and it also optimized the type of kernel function and the kernel parameter setting of support vector regression to establish an effective hybrid optimization support vector regression runoff forecasting model. The proposed method provided an effective way for the choice of kernel functions and parameter optimization. By analyzing the examples of Guangxi Liujiang's River runoff and with pure support vector regression model comparison, the results of the study show that the model is stable in prediction and it has high generalization performance and accuracy of prediction, and it can provide an effective prediction method for runoff forecast.

[2019-09-05] Printed Article /doc/2018.03.0148/v2