基于相空间重构的支持向量机方法在径流中长期预报中应用  

Application of support vector machine based on phase-space reconstruction to medium-term and long-term runoff forecast

作　　者：刘冀[1] 王本德[1] 袁晶瑄[1] 周惠成[1] 

机构地区：[1]大连理工大学土木水利学院,辽宁大连116024

出　　处：《大连理工大学学报》2008年第4期591-595,共5页Journal of Dalian University of Technology

基　　金：国家自然科学基金委与二滩水电开发有限公司雅砻江水电联合研究基金资助项目(50579095)

摘　　要：水文中长期预报对于水资源规划管理、水库及水电站调度具有十分重要的意义.针对常规混沌预测方法的局限性,提出基于相空间重构的支持向量机(SVM)预报方法.该方法首先对径流时间序列进行混沌辨识,然后对其进行相空间重构,采用基于结构风险最小化的SVM进行径流预报.对于SVM的参数优选问题,以径向基核函数作为核函数,采用混沌变尺度优化方法进行参数寻优.实例表明,该方法优于SVM和人工神经网络(ANN)预报方法,且具有良好的泛化推广能力.Effective medium-term and long-term runoff forecast is of great significance to the successful water resources planning and management, reservoirs and hydropower stations operation. The combined method, called support vector machine （SVM） based on the phase-space reconstruction, is developed according to the limitation of the past chaotic forecasting methods. Firstly, the existence of chaos in runoff time series is determined. Secondly, phase-space reconstruction of the runoff series is conducted and SVM, based on a principle that aims at minimizing the structural risk, is employed for forecasting, and the radial basis kernel is used as kernel function. Mutative scale chaos optimization algorithm is employed to search optimal parameters of the SVM. Comparison of the proposed method and artificial neural network indicates that the former is superior to the latter both in the forecasting accuracy and generalization ability.

关 键 词：径流中长期预报 相空间重构 支持向量机 混沌优化 人工神经网络 

分 类 号：P338[天文地球—水文科学]