长江科学院院报 ›› 2014, Vol. 31 ›› Issue (9): 29-32.DOI: 10.3969/j.issn.1001-5485.2014.09.006

• 工程安全与灾害防治 • 上一篇    下一篇

基于周期外延法的监测效应量灰色时序组合预测模型

王振双a,施玉群a,何金平a,b   

  1. 武汉大学 a.水利水电学院;b.水资源与水电工程科学国家重点实验室, 武汉 430072
  • 收稿日期:2013-07-08 修回日期:2014-09-04 出版日期:2014-09-01 发布日期:2014-09-04
  • 通讯作者: 施玉群(1967-),女,云南昭通人,副教授,研究方向为水利水电工程管理,(电话)027-68772191(电子信箱)whusyq@whu.edu.cn。
  • 作者简介:王振双(1990-),男,河南新乡人,硕士研究生,主要从事大坝安全监测与健康诊断研究,(电话)027-68772221(电子信箱)xinxiangwzs@163.com。
  • 基金资助:
    国家自然科学基金资助项目(51379162;51079114)

Combinatorial Forecast Model of Monitoring Effect Quantities Based on Periodic Extensional Method and Grey-Time Serial Model

WANG Zhen-shuang1, SHI Yu-qun1, HE Jin-ping1,2   

  1. 1. School of Water Resources and Hydropower, Wuhan University, Wuhan 430072, China;
    2. State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University, Wuhan 430072, China
  • Received:2013-07-08 Revised:2014-09-04 Online:2014-09-01 Published:2014-09-04

摘要: 针对单一模型在大坝效应量监测数据序列拟合和预测方面存在的不足,采用Verhulst模型拟合监测数据序列中的趋势性成分,采用周期外延模型拟合监测数据序列中的周期性成分,采用自回归AR(p)模型拟合监测数据序列中的随机性成分,得到一种新的组合模型,并给出了一个工程实例。该组合模型丰富了监测效应量预测方法,可提高监测效应量的整体预测精度,深化对监测效应量变化规律的认识。

关键词: 大坝监测, 组合预测, Verhulst模型, 周期外延模型, AR(p)模型

Abstract: In view of the shortcomings of single model used to simulate and forecast the data sequence of dam monitoring effect quantities, a new combinatorial model is constructed and an engineering example is given in this paper. In this combinatorial model, trend component, periodic component and random component of the monitoring data sequence are respectively simulated by Verhulst model,periodic extensional model and AR(p) model. The forecast methods for monitoring effect quantities can be enriched and the overall forecast accuracy can be improved with this combinatorial model, and our understanding of the variation regularity of monitoring effect quantities can also be deepened.

Key words: dam monitoring, combinatorial forecast, Verhulst model, periodic extensional model, AR(p) model

中图分类号: