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DING Geyuan. DISPLACEMENT PREDICTION OF THE ZHUJIADIAN LANDSLIDE WITH OUTLIERS[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 28(1): 132-140. doi: 10.13544/j.cnki.jeg.2019-235
Citation: DING Geyuan. DISPLACEMENT PREDICTION OF THE ZHUJIADIAN LANDSLIDE WITH OUTLIERS[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 28(1): 132-140. doi: 10.13544/j.cnki.jeg.2019-235

DISPLACEMENT PREDICTION OF THE ZHUJIADIAN LANDSLIDE WITH OUTLIERS

doi: 10.13544/j.cnki.jeg.2019-235
Funds:

the National Natural Science Foundation of China 41630643

  • Received Date: 03 Jun 2019
  • Rev Recd Date: 04 Jul 2019
  • Publish Date: 25 Feb 2020
  • Landslide displacement prediction is one of important parts of landslide disaster prevention and mitigation. To improve the accuracy and precision of landslide displacement prediction is emphasis and difficulty. Outliers from monitoring samples are took into account in this research. By ignoring, reserving or correcting outliers to study on which is the best of three ways of landslide displacement prediction with outliers. The Zhujiadian Landslide in Three Gorges Reservoir Region is chosen as the case study on displacement prediction. Based on ARIMA(p, d, q) model, predictions are carried out using the accumulated displacement and the displacement rate time series, respectively. The research results show that: (1) The landslide prediction result with correcting outliers is between ignoring and reserving ones. (2)For ARIMA model, it is more suitable for using the displacement rate time series. (3)The prediction results of "step-like" displacement rates based on ARIMA(1, 0, 1) model for the displacement rate time series with correcting outliers are 79.0mm and 70.2mm in Jun. 2016 and 2017, and accumulated displacement is 1647.7mm until Aug. 2017.

     

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  • Dong D M, Liang Y, Wang L Q, et al. 2017. Displacement prediction method based on ensemble empirical mode decomposition and support vector machine regression—a case of landslides in Three Gorges Reservoir area[J]. Rock and Soil Mechanics, 38(12): 366-3669.
    Hu X L, Zhang M, Sun M J, et al. 2015. Deformation characteristics and failure mode of the Zhujiadian landslide in the Three Gorges Reservoir, China[J]. Bulletin of Engineering Geology and the Environment, 74(1): 1-12. doi: 10.1007/s10064-013-0552-x
    Lei D X, Yi W. 2018. Prediction and forecast of landslide displacement based on ARIMA model: case of Wangjiapo landslide in Three Gorges Reservoir area[J]. Yangtze River, 49 (21): 56-60, 83. http://d.old.wanfangdata.com.cn/Periodical/rmcj201821011
    Li C D, Tang H M, Hu X L, et al. 2009. Landslide prediction based on wavelet analysis and cusp catastrophe[J]. Journal of Earth Science, 20(6): 971. doi: 10.1007/s12583-009-0082-4
    Li J, Zhang Z J, Hiu R Q, et al. 2016. Prediction of landslide displacement based on ARIMA-MC model[J]. Computer Engineering and Applications, 52(7): 215-221. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgcyyy201607040
    Li S W, Li Y C. 2015. Landslide displacement prediction based on ARIMA model[J]. Journal of Chengdu University(Natural Science Edition), 34(4): 421-424. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201704016
    Ma J W. 2016. Rvolution characteristics of multiple physics-based parameters and data mining technology for progressive landslides[J]. Wuhan: China University of Geosciences(Wuhan).
    Ma J W, Tang H M, Liu X, et al. 2017. Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China[J]. Landslides, 14(3): 1275-1281. doi: 10.1007/s10346-017-0804-0
    Miao F S, Wu Y P, Xie Y H, et al. 2017. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model[J]. Landslides, 15(3): 1-14. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=04ecaca2e44c7cf99ebf484dc17fa242
    Miao F S, Wu Y P, Xie Y H, et al. 2016. Displacement prediction of baishuihe landslide based on multi algorithm optimization and SVR model[J]. Journal of Engineering Geology, 24(6): 1136-1144. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201606016
    Tan F L, Hu X L, He C C, et al. 2018. Identifying the main control factors for different deformation stages of landslide[J]. Geotechnical and Geological Engineering, 36(1): 469-482. doi: 10.1007/s10706-017-0340-7
    Tan F L. 2018. Evaluation method for dynamic stability of landslide stabilizing pile system with different evolution modes[J]. Wuhan: China University of Geosciences(Wuhan).
    Xu F, Wang Y, Du J, et al. 2011. Study of displacement prediction model of landslide based on time series analysis[J]. Chinese Journal of Rock Mechanics and Engineering, 30(4): 746-751.
    Yan H, Li S H, Wu L Z. 2019. Landslide displacement prediction based on multiple data-driven model methods[J]. Journal of Engineering Geology, 27(2): 459-465. http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201902028
    Zhao H, Shao S H, Xie D P. 2004. Examination method for qutlier of analytical data[J]. Journal of Zhoukou Teachers College, 21(5): 70-71. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zksfgdzkxxxb200405021
    Zhou C, Yin K L, Cao Y, et al. 2016. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China[J]. Engineering Geology, 204 : 108-120. doi: 10.1016/j.enggeo.2016.02.009
    Zhu J X, Bao Y T, Li Z. 2018. Discussion on the method for testing and treating outliers[J]. University Chemistry, 33(8): 58-65. http://d.old.wanfangdata.com.cn/Periodical/dxhx201808012
    邓冬梅, 梁烨, 王亮清, 等. 2017.基于集合经验模态分解与支持向量机回归的位移预测方法:以三峡库区滑坡为例[J].岩土力学, 38(12): 3660-3669. http://d.old.wanfangdata.com.cn/Periodical/ytlx201712035
    雷德鑫, 易武. 2018.基于ARIMA时间序列模型的滑坡位移预测预报——以三峡库区王家坡滑坡为例[J].人民长江, 49 (21): 60-64, 87. http://www.cnki.com.cn/Article/CJFDTotal-RIVE201821011.htm
    李炯, 张志军, 牛瑞卿, 等. 2016.基于ARIMA-MC模型的滑坡位移预测[J].计算机工程与应用, 52(7): 215-221. doi: 10.3778/j.issn.1002-8331.1404-0187
    李首位, 李益陈. 2015.基于ARIMA模型的滑坡位移预测[J].成都大学学报(自然科学版), 34(4): 421-424. doi: 10.3969/j.issn.1004-5422.2015.04.026
    马俊伟. 2016.渐进式滑坡多场信息演化特征与数据挖掘研究[D].武汉: 中国地质大学(武汉). http://cdmd.cnki.com.cn/Article/CDMD-10491-1016312151.htm
    苗发盛, 吴益平, 谢媛华, 等. 2016.基于多算法参数优化与SVR模型的白水河滑坡位移预测[J].工程地质学报, 24(6): 1136-1144. doi: 10.13544/j.cnki.jeg.2016.06.013
    谭福林. 2018.基于不同演化模式的滑坡-抗滑桩体系动态稳定性评价方法研究[D].武汉: 中国地质大学(武汉). http://cdmd.cnki.com.cn/Article/CDMD-10491-1018817136.htm
    王玉荣. 2004. ARIMA模型在我国出口贸易预测中的应用[J].统计与决策, (4): 33-34. doi: 10.3969/j.issn.1002-6487.2004.04.019
    徐峰, 汪洋, 杜娟, 等. 2011.基于时间序列分析的滑坡位移预测模型研究[J].岩石力学与工程学报, 30(4): 746-751. http://d.old.wanfangdata.com.cn/Periodical/yslxygcxb201104012
    鄢好, 李绍红, 吴礼舟. 2019.联合多种数据驱动建模方法的滑坡位移预测研究[J].工程地质学报, 27(2): 459-465. doi: 10.13544/j.cnki.jeg.2017-485
    赵辉, 邵素华, 谢东坡. 2004.分析数据中离群值的处理方法[J].周口师范学院学报, 21(5): 70-71. doi: 10.3969/j.issn.1671-9476.2004.05.021
    中华人民共和国行业标准编写组. 2008.数据的统计处理和解释正态样本离群值的判断和处理(GB/T 4883-2008)[S].北京: 中国标准出版社.
    朱嘉欣, 包雨恬, 黎朝. 2018.数据离群值的检验及处理方法讨论[J].大学化学, 33 (8): 58-65. http://d.old.wanfangdata.com.cn/Periodical/dxhx201808012
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