基于改进相空间重构原理的支持向量机月径流模拟

Monthly runoff simulation of support vector machine based on principle of improvement and phase space reconstruction

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。针对相空间重构中确定延迟时间τ和嵌入维数m的方法众多,且各方法确定结果不尽相同等缺点,本文采用试凑的方法,在延迟时间τ和嵌入维数m取值范围为2～10内依次构建81个基于相空间重构理论的CV-SVM月径流模拟模型,以南利河董湖站月径流模拟为例进行分析,确定最佳延迟时间τ和嵌入维数m,并与自相关函数法等相关延迟时间τ和嵌入维数m的确定方法确定结果进行比较,旨在探寻延迟时间τ和嵌入维数m对月径流模拟精度的影响及其规律,为基于时间序列的水文模拟及预测预报提供方法和参考。

Based on the CV - SVM principle and methods, the paper constructed the runoff time series model by combining phase space reconstruction theory with support vector machine. According to the many methods of phase space reconstruction to determine the delay time ~＂ and embedding dimension m , and the shortcomings of determining different results by every method, this paper adopted the method of trial and error, the delay time ~＂ and embedding dimension m value range from 2 to 10 in order to construct 81 monthly runoff simulation model based on the theory of phase space reconstruction CV - SVM. Taking monthly runoff simulation in Nanli River Donghu Lake station for example, it determined the optimal de- lay time r and embedding dimension m and compared the results with that determined by autocorrelation function. The aim is to explore the influence of delay time r and embedding dimension m of monthly runoff simulation on accuracy and its regularity, and provide method and reference for the hydrological simula- tion and forecast of time series.