空间自回归模型在水库边坡位移预测中的应用
发布时间：2019-03-18 17:22
【摘要】：针对传统位移监测很少考虑不同测点之间相互作用的问题,基于经济学领域空间计量学基本理论,研究了空间自回归模型在边坡位移预测中的应用。以某工程高边坡外观位移数据为例,对边坡的位移状况进行预测,并与传统的自回归积分滑动平均模型相比较。结果表明:(a)在空间自相关系数较为显著的条件下,运用空间自回归模型可以较为精确地预测边坡变形状况,且优于传统模型;(b)空间自回归模型相较于传统模型参数更加简洁、考虑的影响因素更全面,可以同时对空间所有测点位移进行估计。
[Abstract]:In view of the problem that the traditional displacement monitoring seldom takes into account the interaction between different measuring points, the application of the spatial self-regression model in the slope displacement prediction is studied based on the basic theory of the space metrology in the field of economics. Taking the displacement data of high side slope of a certain project as an example, the displacement condition of the side slope is predicted, and compared with the traditional self-regression integral sliding average model. The results show that: (a) the spatial autoregressive model can predict the slope deformation more accurately under the condition that the spatial autocorrelation coefficient is significant, and the model is superior to the traditional model; (b) the space self-regression model is more concise than the traditional model parameters, The influence factors considered are more comprehensive, and the displacement of all measuring points in the space can be estimated at the same time.
【作者单位】： 大唐环境产业集团股份有限公司大唐(北京)水务工程技术有限公司;
【分类号】：TV698.11
[Abstract]:In view of the problem that the traditional displacement monitoring seldom takes into account the interaction between different measuring points, the application of the spatial self-regression model in the slope displacement prediction is studied based on the basic theory of the space metrology in the field of economics. Taking the displacement data of high side slope of a certain project as an example, the displacement condition of the side slope is predicted, and compared with the traditional self-regression integral sliding average model. The results show that: (a) the spatial autoregressive model can predict the slope deformation more accurately under the condition that the spatial autocorrelation coefficient is significant, and the model is superior to the traditional model; (b) the space self-regression model is more concise than the traditional model parameters, The influence factors considered are more comprehensive, and the displacement of all measuring points in the space can be estimated at the same time.
【作者单位】： 大唐环境产业集团股份有限公司大唐(北京)水务工程技术有限公司;
【分类号】：TV698.11
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1 陈建良;自回归模型的参数估计[J];山东工业大学学报;1996年03期
2 程懋华,高N，
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