回归支持向量机模型及其在年径流预测中的应用

Application of model of regression support vector machine in prediction of annual runoff

研究交叉验证(CV)SVR年径流预测模型,以云南省清水江革雷站为例进行实例分析。利用SPSS软件选取年径流影响因子,确定输入向量;采用CV方法搜寻SVR惩罚因子C和核函数参数g,构建CV-SVR多元变量年径流预测模型,并构建GA-BP、传统BP模型作为对比模型。利用所构建的模型对实例进行预测。结果表明:CVSVR模型对实例后15年年径流预测的平均相对误差绝对值和最大相对误差绝对值分别为3.4596%、9.3035%,预测精度和泛化能力均优于GA-BP、传统BP模型,表明CV能有效搜寻SVR惩罚因子C和核函数参数g。CV-SVR模型具有预测精度高、泛化能力强以及算法稳定等特点。

Taking Gelei station of the Qingshui River in Yunnan for example,the paper researched the cross validation（ CV） SVR prediction model of annual runoff. It chose the influence factor of annual runoff and determined the input vector by using SPSS software; CV method was used to search the SVR penalty factor and kernel parameter,and constructed the prediction model of annual runoff of multivariate CV-SVR,and GA-BP model,traditional BP model as a comparable model. The results show that the absolute value of the average relative error and the maximum absolute value of relative error of prediction of 15 annual runoff example by CV-SVR model are 3. 4596%,9. 3035% respectively. the prediction accuracy and generalization ability of CV-SVR are better than that of GA-BP and traditional BP model. CV method can effectively search the SVR penalty factor and kernel parameter. CV-SVR model has many characteristics of high forecast precision,strong generalization ability and stable algorithm method.