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山东大学学报(工学版)

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地下水位预报中的组合时间序列分析法

廖伙木1, 董增川1,  束龙仓2, 贠汝安3   

  1. 1. 河海大学水文水资源及水利工程科学国家重点实验室, 江苏 南京 210098;2. 河海大学水资源环境学院, 江苏 南京 210098;3. 山东大学 土建与水利学院, 山东 济南 250061
  • 收稿日期:2007-06-25 修回日期:1900-01-01 出版日期:2008-04-16 发布日期:2008-04-16
  • 通讯作者: 廖伙木

Combinative time series analysis method for the prediction  of the groundwater level

LIAO Huo-mu1, DONG Zeng-chuan1, SHU Long-cang2, YUN Ru-an3   

  1. 1. State Key Laboratory of Hydrology on Water Resource and Hydraulic Engineering, Hohai University, Nanjing 210098, China; 2. The College of Water Resources and Environment, Hohai University
  • Received:2007-06-25 Revised:1900-01-01 Online:2008-04-16 Published:2008-04-16
  • Contact: LIAO Huo-mu

摘要: 将系统分析方法中的传统GM(1,1)模型与时间序列分析方法相结合建立地下水位的预报模型. 为更好地反映地下水位的统计规律随时间而缓慢变化的现象,采用衰减记忆最小二乘法率定GM(1,1)模型参数. 用方差分析法进行地下水位时间序列的周期分析时,对初相进行优选;在选定的置信度水平下,当有多个周期都通过检验,此时该如何优先选择哪个周期,文中提出按拟选周期的F分布检验统计量与检验区间界限值的比值最大原则来选择周期成分. 最后,采用本文所述方法进行福建省龙岩市的年最高地下水位的预报.

关键词: 地下水位预报, GM(1, 1)模型, 方差分析, 初相优选, 时间序列分析

Abstract: Traditional GM(1,1) and time series analysis method were integrated together to establish the groundwater level forecast model.  The fading memory least square methods was applied in the process of confirming the parameters of GM(1,1) in order to more factually reflect the phenomena of the groundwater level's statistic rule varying with the time. The periods components were analyzed with the variance analysis method and the starting phases were optimized. Under the condition that more than one F-statistic values of periods were over the critical values of the selected confidence degree, what rules can be adopted to decide which period should be first selected. The rule was put forward that the ratio of F-statistic value of  selected period's being divided by the critical value of the examination interval should be the greatest. Finally, the yearly highest groundwater levels in Longyan Basin in the west of  Fujian Province were forecast with this method.

Key words: groundwater level forecast, GM(1, 1), variance analysis, starting phase optimization, time series analysis

中图分类号: 

  • TV213
[1] 边培松1,王登杰1,于少华2. 新陈代谢GM(1,1)模型在建筑物沉降预测中的应用[J]. 山东大学学报(工学版), 2010, 40(3): 119-123.
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