›› 2016, Vol. 37 ›› Issue (S2): 552-560.doi: 10.16285/j.rsm.2016.S2.070

• 岩土工程研究 • 上一篇    下一篇

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

孟 蒙1, 2,陈智强3,黄 达1, 2,曾 彬2,陈赐金4   

  1. 1. 重庆大学 煤矿灾害动力学与控制国家重点实验室,重庆 400044;2. 重庆大学 土木工程学院,重庆 400045; 3. 重庆市地质矿产勘查开发局107地质队,重庆 401120;4. 巫山县地质灾害整治中心,重庆 404700
  • 收稿日期:2016-04-01 出版日期:2016-11-11 发布日期:2018-06-09
  • 通讯作者: 黄达,男,1976年生,博士,教授,博士生导师,主要从事岩体力学及地质灾害方面的研究与教学工作。E-mail: hdcqy@126.com
  • 作者简介:孟蒙,男,1990年生,硕士研究生,主要从事滑坡地质灾害研究。
  • 基金资助:
    国家自然科学基金(No.41472245);重庆市国土房管科技计划项目(No.CQGT-KJ-2014049);中央高校基本科研业务费重大项目(No.106112016CDJZR208804)。

Displacement prediction of landslide in Three Gorges Reservoir area based on H-P filter, ARIMA and VAR models

MENG Meng1, 2, CHEN Zhi-qiang3, HUANG Da1, 2, ZENG Bin2, CHEN Ci-jin4   

  1. 1. State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China; 2. College of Civil Engineering, Chongqing University, Chongqing 400045, China; 3. No.107 Team of Chongqing Geology Exploring Bureau, Chongqing 401120, China; 4. Geological Disaster Control Center of Wushan County, Chongqing 404700, China
  • Received:2016-04-01 Online:2016-11-11 Published:2018-06-09
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (41472245), Chongqing Administration of Land, Resources and Housing (CQGT-KJ-2014049), and the Fundamental Research Funds for the Central Universities (106112016CDJZR208804).

摘要: 受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型(ARIMA)对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型(VAR)计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。

关键词: 滑坡, 变形预测, 时间序列, H-P滤波法, 差分自回归滑动平均(ARIMA)模型, 向量自回归(VAR)模型

Abstract: Landslide displacement in Three Gorges Reservoir area is of periodicity due to water level change, rainfall and so on. Based on the time series analysis, landslide displacement can be divided into the trend displacement reflecting the long-term trend of landslide, which is the response of geologic structure; and the periodic displacement reflecting the volatility of landslide, which is mainly affected by external factors such as rainfall. Taking Taping landslide in Three Gorges Reservoir area for example and considering the influences of water level change and rainfall, the trend displacement and periodic displacement are evaluated by Hodrick-Prescott (H-P) filter forecasting method. Difference auto-regressive integrated moving average (DARIMA) model is utilized to smooth the curve of trend displacement, and then compute the predicted value of trend displacement. Vector auto-regressive (VAR) model is used to predict the periodic displacement. The overall predicted displacement is obtained by adding the predicted values of trend displacement and periodic displacement, which is compared with the monitoring displacement and one predicted by other forecasting methods. The results show that the predicted displacements by this proposed method are in better agreement with the monitoring data; the proposed comprehensive model can better reflect the trend and volatility of landslide displacement.

Key words: landslide, displacement prediction, time series, H-P filter method, auto-regressive integrated moving average (ARIMA) model, vector auto-regressive (VAR) model

中图分类号: 

  • P 642.22
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