李广春, 戴吾蛟, 杨国祥, 刘斌. 时空自回归模型在大坝变形分析中的应用[J]. 武汉大学学报 ( 信息科学版), 2015, 40(7): 877-893. DOI: 10.13203/j.whugis20130549
引用本文: 李广春, 戴吾蛟, 杨国祥, 刘斌. 时空自回归模型在大坝变形分析中的应用[J]. 武汉大学学报 ( 信息科学版), 2015, 40(7): 877-893. DOI: 10.13203/j.whugis20130549
LI Guangchun, DAI Wujiao, YANG Guoxiang, LIU Bin. Application of Space-Time Auto-Regressive Model in Dam Deformation Analysis[J]. Geomatics and Information Science of Wuhan University, 2015, 40(7): 877-893. DOI: 10.13203/j.whugis20130549
Citation: LI Guangchun, DAI Wujiao, YANG Guoxiang, LIU Bin. Application of Space-Time Auto-Regressive Model in Dam Deformation Analysis[J]. Geomatics and Information Science of Wuhan University, 2015, 40(7): 877-893. DOI: 10.13203/j.whugis20130549

时空自回归模型在大坝变形分析中的应用

Application of Space-Time Auto-Regressive Model in Dam Deformation Analysis

  • 摘要: 变形监测分析的模型与方法主要是针对单点时序的分析,建立大坝位移自回归模型可实现大坝位移预测预报,但传统自回归模型都是针对单测点进行的,这意味着需要对所有的测点进行建模,将会造成大量模型冗余.而大坝作为一个整体结构,测点间的位移在空间上是相互关联的。单点自回归模型并未考虑着这种相关性,为了考虑测点间的这种空间相关性并建立统一的模型,本文采用时空自回归方法对五强溪大坝位移监测数据进行整体分析,建立了大坝位移的时空自回归模型。通过对大坝引张线测点的建模与预测分析,结果表明时空自回归模型在时间和空间上都可以对位移监测数据序列进行较好的拟合与预测。

     

    Abstract: Modelsandmethodsofanalysisfordeformationmonitoringarealwayssetforthetimeseriesdataofonemonitoringpoint.Anauto regressivemodelcanbeusedformodelingdamdeformationtoforecastthedisplacement.Inthetraditionalmethod,itisnecessarytoestablishtheauto regressivemodelforeverymonitoringpoint.Tosomeextent,thetraditionalmethodhastomodeldifferentmod elsforeverymonitoringpointonthedamwhichmakeusdothesamethingmanytimes.However,asawholestructure,monitoringpointsonadamarerelatedtoeachother.Thetraditionaltimeauto re gressivemodeldoesnotconsidertherelationshipbetweendifferentmonitoringpointsonthedam.Toconsiderthespatialcorrelationbetweenmeasuringpointsandtherebyestablishaunifiedmodel,aspace timeauto regressivemethodisusedtomodelthedisplacementsoftheWuqiangxidam.Space timeauto regressionisjustusedonceforallthemonitoringpointsonthedamwhichsavetimeofpro cessingspace timeseries.Byanalyzingandforecastingthehorizontaldisplacementsofthedam,ourtestresultsshowthataspace timeauto regressivemodelcanbeusedforfittingandforecastingdis placementseriesbothinthetemporalandspatialdomains.

     

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