A Solution for Ground Subsidence Prediction of Time Series Based on Autoregression EIV Model with Inequality Constraints

被引：0
|
作者：
Tao Y. [1 ]
Wang J. [2 ]
Liu C. [3 ]
机构：
[1] School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an
[2] School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing
[3] School of Surveying and Mapping, Anhui University of Science and Technology, Huainan
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2020年 / 45卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Autoregression EIV model; Inequality constraints; Nested iteration; Time series of ground subsidence;
D O I：
10.13203/j.whugis20180268
中图分类号：
学科分类号：
摘要：
Ground subsidence monitoring is an effective method to forecast geological hazard, and time series model is the main model for ground subsidence prediction. To take into account both the observation errors existing in coefficient matrix and observation vector, in this contribution, the time series model of ground subsidence is developed to improve errors-in-variables (EIV) model, while the traditional model only takes into account the observation error existing in observation error. Besides, to improve efficiency and accuracy of computation model parameters, prior information is utilized to establish EIV model with inequality constraints, and the inequality constraints model for ground subsidence prediction is converted into quadratic programming of nonlinear model. And the iterative algorithm which is combined with median function is proposed. The efficiency and feasibility of the presented algorithm are verified through the instances, which are compared with the traditional least squares estimation algorithm and current algorithm for EIV model. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
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页码：1455 / 1460
页数：5
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