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

TAO Yeqing; WANG Jian; LIU Chao

doi:10.13203/j.whugis20180268

Volume 45 Issue 9

Sep. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(9): 1455-1460. > DOI: 10.13203/j.whugis20180268

TAO Yeqing, WANG Jian, LIU Chao. A Solution for Ground Subsidence Prediction of Time Series Based on Autoregression EIV Model with Inequality Constraints[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1455-1460. DOI: 10.13203/j.whugis20180268

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

1.
School of Urban and Environmental Sciences, Huaiyin Normal University, Huai'an 223300, China
2.
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
3.
School of Surveying and Mapping, Anhui University of Science and Technology, Huainan 232001, China

Funds:

The National Natural Science Foundation of China 41601501

The National Natural Science Foundation of China 41874029

The National Natural Science Foundation of China 41404004

the Natural Science Foundation for Colleges and Universities of Jiangsu Province 16KJD420001

Science Foundation of Jiangsu Provincial Department of Construction 2017ZD259

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Author Bio:
TAO Yeqing, PhD, associate professor, specializes in the theories and methods of surveying adjustment. E-mail: yenneytao@163.com
Received Date: July 12, 2018
Published Date: September 04, 2020

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Abstract

Abstract

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.
- time series of ground subsidence,
- inequality constraints,
- autoregression EIV model,
- nested iteration

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

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