关键词: 中长期径流预报, 参数优化, 支持向量机, 萤火虫算法, 小波去噪, 紫坪铺水库,

Abstract: To optimize parameters of support vector machine(SVM)model and enhance mid-long term runoff forecasting accuracy,we established a mid-long term runoff forecasting model based on FA-SVM.In this work,the minimum mean square deviation of the training period data was taken as the objective function and Firefly Algorithm(FA)was used to optimize three main parameters(i.e.,penalty coefficient C,kernel parameter g and insensitive loss coefficient ε)of SVM model.Also,we adopted wavelet de-noising method to de-noise monthly runoff data.Then,both FA-SVM model and BP neural network model were used to forecast mid-long term runoff in Zipingpu Reservoir in the upper reaches of the Minjiang River.The results showed:(1)Wavelet de-noising method could be used to filter the system noise and measurement noise of monthly runoff data.(2)The FA-SVM model worked well for mid-long term runoff forecasting,and its forecast accuracy grade was above grade C.(3)The comprehensive runoff forecasting effectiveness of FA-SVM model was superior to that of BP neural network model.

Key words: mid-long term runoff forecasting, parameter optimization, Support Vector Machine, Firefly Algorithm, wavelet de-noising, Zipingpu Reservoir,