多监测点滑坡变形预测的似乎不相关模型研究

doi:10.13722/j.cnki.jrme.2015.1038

Abstract
Figure/Table
References (19)
Related Citation (15)

Download: PDF (903 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the process of landslide deformation monitoring，multiple monitoring points were arranged on the same landslide usually. These data of monitoring points reflected the trend of the landslide deformation and destruction from different sides，so there was a certain correlation among data of monitoring points. However，this correlation has not been considered into research on the same landslide deformation prediction of multiple monitoring points. Seemingly unrelated regressions(sur) model established in this paper could effectively deal with the correlation among data of multiple monitoring points，and could process the cumulative displacement data of multiple monitoring points in one model，and then more accurate results of deformation prediction of landslide were obtained. As an example，two regression equations were established based on the cumulative displacement data of monitoring points ZG93 and ZG118 of baishuihe landslide，then according to two-step feasible generalized least squares(FGLS)，covariance matrix of error of two regression equations was obtained. The regression equations were actually related due to the covariance matrix of error was non-diagonal，which met the conditions of SUR model，so SUR model of the two monitoring points could be created to complete the deformation prediction of two monitoring points ZG93 and ZG118 synchronously. Finally compared with the results of ordinary least squares(OLS)，parameter estimation of SUR model was closer to the true value and the average relative error of SUR model was less than OLS model，so the prediction accuracy of SUR model is higher than OLS model.

Key words： engineering geology seemingly unrelated regressions multiple monitoring points ordinary least squares relative error

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LUO Wenqiang
	JI Yanan
	WANG Chunyue
	SONG Zijian

Cite this article:

LUO Wenqiang,JI Yanan,WANG Chunyue, et al. Research on seemingly unrelated regressions model of landslide displacement prediction of multiple monitoring points[J]. , 2016, 35(S1): 3051-3056.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2015.1038 OR https://rockmech.whrsm.ac.cn/EN/Y2016/V35/IS1/3051

[1]	贺小黑，王思敬. Verhulst滑坡预测预报模型的改进及其应用[J]. 岩土力学，2013，34(增1)：355-364.(HE Xiaohei，WANG Sijing. improvement of verhulst forecast model of landslide and its application[J]. Rock and Soil Mechanics，2013，34(Supp.1)：355-364.(in Chinese))
[2]	ZELLNERA. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias[J]. Joumal of the American Statistical Association，1962，57(298)：348-368.
[3]	ZELLNER A. Estimators for seemingly unrelated regression equations：some exact finite sample results[J]. Journal of the American Statistical Association，1963，58(304)：977-992.
[4]	梁洪川. 似乎不相关回归模型及其在医学中的应用[博士学位论文][D]. 太原：山西医科大学，2006.(LIANG Hongchuan. Seemingly unrelated model and its application in medicine[Ph. D.Thesis][D]. Taiyuan：Shanxi Medical University，2006.(in Chinese))
[5]	徐文科，刘洋. 捕食与被捕食种群似乎不相关模型的参数估计[J]. 黑龙江大学自然科学学报，2013，30(5)：570-575.(Xuwenke，LIU Yang. parameter estimation of seemingly unrelated model for predator-prey population system[J]. Journal of natural science of Heilongjiang university，2013，30(5)：570-575.(in Chinese))
[6]	唐守正. 生物数学模型的统计学基础[M]. 北京：科学出版社，2002：102-130.(TANG Shouzheng. Statistical basis of biological mathematical model[M]. Beijing：Science Press，2002：102-130.(in Chinese))
[7]	孙继华，郎奎建. 广义森林生态效益似乎不相关模型的总体估计[J]. 北京林业大学学报，2004，26(3)：19-23.(SUN Jihua，LANG Kuijian. Design of apparently not related models with forest ecological benefits in broad sense[J]. Journal of Beijing Forestry University，2004，26(3)：19-23.(in Chinese))
[8]	梁洪川，韩宏，郎素平，等. 似乎不相关模型及其在老年认知功能研究中的应用[J]. 中国卫生统计，2005，22(6)：362-372.(LIANG Hongchuan，HAN Hong，LANG Suping，et al. seeming unrelated
	regressions model and its application in the study of the cognition ofaged people[J]. Chinese Health Statistics，2005，22(6)：362-372.(in Chinese))
[9]	王震宇，孟陆波. 滑坡预报的多元回归分析方法[J]. 中国地质灾害与防治学报，2003，14(3)：21-23.(WANG Zhenyu，MENG Lubo. The multivariate regression analysis method for landslide prediction[J]. The Chinese Journal of Geological Hazard and Control，2003，14(3)：21-23.(in Chinese))
[10]	向东进，李宏伟，刘小雅. 实用多元统计分析[M]. 武汉：中国地质大学出版社，2005：33-44.(XIANG Dongjin，LI Hongwei，LIU Xiaoya. Applied multivariate statistical analysis[M]. Wuhan：China University of Geosciences Press，2005：33-44.(in Chinese))
[11]	张绘画，张汝忠，徐志宏. 马尾松主要蛀干害虫林间诱捕序列的似乎不相关分析[J]. 东北林业大学学报，2012，40(11)：135-138.(ZHANG Huihua，ZHANG Ruzhong，XU Zhihong. analysis of trapping series of main stem borers in pinus massoniana by seemingly unrelated regression[J]. Journal of Northeast Forestry University，2012，40(11)：135-138.(in Chinese))
[12]	BREUSCH TS，PAGAN AR. The Lagrange multiplier test and its application to model specification in econometrics[J]. Review of Economic Studies，1980，47(1)：239-253.
[13]	张俊，殷坤龙，王佳佳，等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报，2015，34(2)：382-391.(ZHANG Jun，YIN Kunlong，WANG Jiajia，et al. Displacement prediction of Baishuihe landslide based on time series and PSO-SVR model[J]. Chinese Journal of Rock Mechanics and Engineering，2015，34(2)：382-391.(in Chinese))
[14]	曾裕平. 重大突发性滑坡灾害预测预报研究[博士学位论文][D]. 成都：成都理工大学，2003.(ZENG Yuping. Study on forecast and prediction of large scale and sudden landslide hazard[Ph. D.Thesis][D]. Chengdu：ChengduUniversity of Technology，2003.(in Chinese))
[15]	沈力，吴益平. 基于信息维的滑坡时间位移序列分析[J]. 地质科技情报，2013，32(2)：187-190.(SHEN Li，WU Yiping. Analysis of landslide time-displacement series based on information dimension[J]. Geological Science and Technology Information，2013，32(2)：187-190.(in Chinese))
[16]	李德营. 三峡库区具台阶状位移特征的滑坡预测预报研究[博士学位论文][D]. 武汉：中国地质大学，2010.(LI Deying. Prediction study of landslide with step-like deformation in the Three Gorges Reservior[Ph. D.Thesis][D]. Wuhan：China University of Geosciences，2010.(in Chinese))
[17]	李飞翱，罗文强，刘小珊，等. 多元非平稳时间序列分析的滑坡变形预测研究[J]. 长江科学院院报，2014，31(4)：31-34.(Lifeiao，luowenqiang，liuxiaoshan，et al. Landslidedeformation prediction by analysis of multivariate non-stationary time series[J]. Journal of Yangtze River Scientific Research Institute，2014，31(4)：31-34.(in Chinese))
[18]	杜鹃，殷坤龙，柴波. 基于诱发因素响应分析的滑坡位移预测模型研究[J]. 岩石力学与工程学报，2009，28(9)：1783-1789.(DU Juan，YIN Kunlong，CHAI Bo. Study of displacement prediction model of landslide based on response analysis of inducing factors[J]. Chinese Journal of Rock Mechanics and Engineering，2009，28(9)：1783-1789.(in Chinese))
[19]	张甜. stata统计分析与行业应用案例详解[M]. 北京：清华大学出版社，2014：158-169.(ZHANG Tian. stata comments of statistical analysis and industry applications[M]. Beijing：Tsinghua University Press，2014：158-169 (in Chinese))