地理科学 ›› 2005, Vol. 25 ›› Issue (6): 720-723.doi: 10.13249/j.cnki.sgs.2005.06.720

• 论文 • 上一篇    下一篇

基于遗传算法的水文时间序列变点分析方法

金菊良1, 魏一鸣2, 丁晶3   

  1. 1. 合肥工业大学土木建筑工程学院, 安徽 合肥 230009;
    2. 中国科学院科技政策 与管理科学研究所, 北京 100080;
    3. 四川大学水电学院, 四川 成都 610065
  • 收稿日期:2004-09-18 修回日期:2005-01-22 出版日期:2005-11-20 发布日期:2005-11-20
  • 基金资助:
    教育部优秀青年教师资助计划[教人司(2002)350]、安徽省优秀青年科技基金、安徽省自然科学基金(01045102,01045409)和国家自然科学基金(70425001,70471090)项目资助。

Genetic Algorithm Based Change-point Analysis Method for Hydrological Time Series

JIN Ju-Liang1, WEI Yi-Ming2, DING Jing3   

  1. 1. School of Civil Engineering, Hefei University of Technology, Hefei, Anhui 230009;
    2. Institute of Policy & Management, Chinese Academy of Sciences, Beijing 100080;
    3. College of Hydraulic Engineering, Sichuan University, Chengdu, Sichuan 610065
  • Received:2004-09-18 Revised:2005-01-22 Online:2005-11-20 Published:2005-11-20

摘要: 为处理常规变点分析方法计算复杂、识别全部变点困难等问题,提出了用遗传算法进行水文时间序列多变点分析的一套新方法(AGA-CPAM)。实例计算的结果说明,AGA-CPAM用于水文时间序列多变点诊断简便、可行和有效,在各种非线性时间序列灾变分析中具有推广实用价值。

Abstract: Analysis of stages and aberrance points of hydrological time series conduces to understand and manage the complex characteristics of hydrological system evolution process, which can be applied in many fields, such as hydrological frequency analysis, hydrological prediction, hydrological computation and so on. In order to overcome shortage of common change-point methods, such as computation complex and the difficulty of diagnosing all change points, a new method of change-point analysis based on accelerating genetic algorithm developed by the authors, named AGA-CPAM for short, is presented for hydrological time series. The modeling of AGA-CPAM is the key in this paper, which includes three steps as follows. Step 1 is to determine the search ranges of change point number and position of hydrological time series according to scatter point plan and dot value figure of hydrological time series. Step 2 is to optimize the parameters of change point positions and jumping values based on the criterion of least square of the subsection fitting errors with accelerating genetic algorithm. Step 3 is to analyse the stages and aberrance points of hydrological time series obtained from Step 2 based on cause of formation, which results can be used as scientific foundation of prediction, simulation, regulation and control of hydrological time series. The computation results of the case study can include two terms as follows: (1)With the accelerating genetic algorithm developed by the authors, both of change-point position values and jumping values can be optimized at the same time, and the difficulty problem of much computation of common change-point analysis methods is solved. (2)The example results show that AGA-CPAM is visual, simple, practical and efficient, and that AGA-CPAM can also be applied to cataclysm change analysis of different nonlinear time series.

中图分类号: 

  • TV122/P343