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。
In consideration of the fact that the interaction between different monitoring points is seldom considered in traditional displacement monitoring, the application of the spatial autoregressive model in the prediction of high slope deformation was examined based on the basic theory of spatial econometrics. Using a high slope as an example, the results of displacement prediction based on the monitoring data of exterior deformation were compared with those from the traditional autoregressive integrated moving average model. The results show that, with a significant spatial autocorrelation coefficient, the spatial autoregressive model is more accurate than the traditional model in prediction of slope deformation, and that, compared with the traditional model, the spatial autoregressive model has more concise parameters, considers the factors more comprehensively, and can perform displacement prediction at different monitoring points at the same time.
出处
《河海大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第2期104-108,共5页
Journal of Hohai University(Natural Sciences)
关键词
空间计量学
空间自回归模型
水库边坡
边坡位移预测
自回归积分滑动平均模型
spatial econometrics
spatial autoregressive model
reservoir slope
slope deformation prediction
au-toregressive integrated moving average model
