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

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,然后对其进行相空阃重构,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,采用混沌交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.

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

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.