基于相空间重构的水文自记忆预测模型  

Self-memory hydrologic prediction model based on phase-space reconstitution

作　　者：李荣峰[1] 沈冰[1] 张金凯 

机构地区：[1]西安理工大学水资源与环境陕西省重点实验室,陕西西安710048 [2]山西省水利水电科学研究院,山西太原030002

出　　处：《水利学报》2006年第5期583-587,共5页Journal of Hydraulic Engineering

基　　金：国家863计划(2002AA2Z4311)

摘　　要：混沌理论为研究复杂多变的非线性水文时间序列开辟了新的途径。本文在相空间重构的基础上,反演了水文系统动力模式,据此进一步建立了相空间自记忆预测模型,并将该模型应用于月径流量预测。实例表明,该模型能较好地处理复杂的水文数据序列,且有较好的预测精度。The theory of chaos is applied to study the complicated hydrological time series with nonlinear characteristics. Based on phase-space roconstitution the nonlinear dynamic mode of hydrologic system is inversely transformed and a self-memory hydrologic prediction model is established. The application of the proposed model to predict the monthly runoff shows that it can effectively dealing with the complicated hydrological time series with preferable precision.

关 键 词：水文时间序列 相空间自记忆模型 动力模式反演 预测方法 

分 类 号：P333[天文地球—水文科学]