长江科学院院报 ›› 2013, Vol. 30 ›› Issue (10): 21-26.

• 水资源与环境 • 上一篇    下一篇

相空间重构支持向量机在径流模拟中的应用研究

李代华1,崔东文2   

  1. 1.云南省水文水资源局 文山分局, 云南文山 663000;
    2.云南省文山州水务局,云南文山 663000
  • 收稿日期:2013-10-16 修回日期:2013-10-16 出版日期:2013-10-15 发布日期:2013-10-16
  • 作者简介:李代华(1976-),女,云南文山人,高级工程师,主要从事水文水资源及水资源保护等工作,(电话)13577633208(电子信箱)wsldh@hotmail.com。

Phase Space Reconstruction of Support Vector Machine in Runoff Simulation

LI Dai-hua1, CUI Dong-wen2   

  1. 1.Wenshan Branch of Yunnan Provincial Hydrology and Water Resources Bureau, Wenshan 663000,China;
    2.Water Authority of Wenshan Autonomous Prefecture of Yunnan Province, Wenshan 663000, China
  • Received:2013-10-16 Revised:2013-10-16 Online:2013-10-15 Published:2013-10-16

摘要:

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eMRE、最大相对误差eMaxRE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。

关键词: 相空间重构, 支持向量机, 交叉验证, 混沌, 径流模拟

Abstract:

The theory of phase space reconstruction is introduced into the monthly runoff simulation. The C-C algorithm 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 Longtanzhai 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.

Key words: phase space reconstruction, support vector machine, cross validation, chaos, runoff simulation

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