遗传算法与最小二乘支持向量机在年径流预测中的应用

为克服最小二乘支持向量机(LSSVM)依赖人为经验选择学习参数的不足,利用遗传优化算法(GA)选择LSSVM惩罚因子C和核函数参数σ2,构建GA-LSSVM年径流预测模型,并构建LSSVM、GA-BP和传统BP模型作为对比,以云南省河边水文站年径流预测为例进行实例研究,利用实例前30 a和后22 a资料分别对各模型进行训练和预测。结果表明:GA-LSSVM模型对实例后22 a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3.13%、8.66%,预测精度优于LSSVM、GA-BP和传统BP模型。GA算法全局寻优能力强,利用GA算法优化得到的LSSVM学习参数可有效提高LSSVM模型的预测精度和泛化能力。

Application of genetic algorithm and least squares support vector machine in prediction of annual runoff

In order to overcome the lack of learning parameters of least square support vector machine (LSSVM) which is chosen by human experience, the paper used the genetic algorithm (GA) to select LSSVM penalty factor c and kernel parameter and construct GA-LSSVM annual runoff forecasting model, and set up LSSVM, GA-BP and traditional BP model as the comparison.Taking Yunnan province river hydrological station annual runoff prediction as a case study , it used the data before 30 years and after 22 years to train and predict every model. The results show that the absolute value of the average relative error of annual runoff and the maximum absolute value of relative error predicted by GA-LSSVM model after 22 years are 3.13%, 8.66%, the prediction accuracy of GA-LSSVM model is better than that of LSSVM, GA-BP and traditional BP model. The global optimization ability of GA algorithm is strong. The LSSVM learning parameters gotten by optimization of GA algorithm can effectively improve the prediction accuracy of LSSVM model and the generalization ability.