相空间重构支持向量机在径流模拟中的应用研究

Phase Space Reconstruction of Support Vector Machine in Runoff Simulation

将相空间重构理论引入月径流模拟中,利用C-C算法进行相空间重构,将一维径流时间序列拓展为多维,基于交叉验证支持向量机(CV-SVM)原理及方法,构建以相空间重构理论与支持向量机相结合的径流时间序列模拟模型,并构建传统BP、双隐层BP及GA-BP径流时间序列模拟模型作为对比模型,以盘龙河龙潭寨月径流时间序列为例进行分析。结果显示:基于相空间重构理论的CV-SVM模拟模型能较好地处理复杂的径流序列,在长达200个月的测试样本模拟中,平均相对误差e MRE、最大相对误差e MaxRE分别为0.571 7%,5.526 7%,决定系数DC和合格率QR分别为0.999 9和100%。表明该模型具有较高的泛化能力和模拟精度,模拟效果明显优于传统BP、双隐层BP模型,甚至优于GA-BP模型;表明研究建立的基于相空间重构理论的CV-SVM模型用于径流模拟是合理可行的,可为径流模拟提供方法和参考。

The theory of phase space reconstruction is introduced into the monthly runoff simulation. The C-C algo- rithm was used for phase space reconstruction, and the one-dimensional runoff time series were expanded to multi- dimensional. Furthermore, with the association of CV-SVM （ cross validation-support vector machine） principle and methods, a runoff time series model was established. Meanwhile, traditional BP, double hidden layer BP and GA- BP runoff time series simulation model were constructed for comparison. The monthly runoff time series at Long- tanzhai of Panlong River was taken as analysis example. The results showed that： the model based on phase space reconstruction and CV-SVM can better handle the complex runoff series. During the simulation of test sample of 200 months, the average relative error eMRE, the maximum relative error eMaxRE , the determination coefficient DC, and the qualified rate QR was respectively 0. 5717%, 5. 5267%, 0. 9999 and 100%, which demonstrated that the model is of high generalization ability and simulation precision. The simulation result is obviously superior to those of traditional BP, double hidden layer BP model, and is even better than GA-BP model. The results indicate that the model based on phase space reconstruction and CV-SVM model for runoff simulation is feasible.