“阶跃式”滑坡位移预测及阈值分析的ARMA-(LASSO-ELM)-Copula模型

doi:10.13722/j.cnki.jrme.2016.1542

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摘要

滑坡位移预测的重点内容是提高预测精度及提取阈值，基于该认识及已有研究，建立ARMA-(LASSO- ELM)-Copula模型，并选取白水河滑坡这一典型“阶跃式”滑坡为研究案例。首先，运用ARMA模型中的ACF值分析月降雨量与月库水位变化的周期性，并通过PPMCC分析位移月增量与一个周期内诱因数据(月降雨量，月库水位变化)的滞后相关性；然后，建立以所有诱因正相关滞后项为预测模型的输入项，以位移月增量为目标的LASSO-ELM滑坡位移预测模型，将预测所得位移增量累加得到位移预测值；最后，基于诱因值与滑坡位移预测值，比选得到最优Copula函数之后，分别建立其联合分布函数，并提取阀值。结果表明，ARMA联合LASSO-ELM的位移预测模型具有较高精度，明显优于SVR、NN、ELM等模型；选取Gumbel-Hougaard Copula函数可描述案例诱因数据与位移预测值的联合分布，并能提取诱因阈值和对应的位移风险值。

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	李骅锦1
	许强1
	何雨森2
	朱星1

关键词 ：边坡工程, 滑坡位移预测, 周期性, ARMA, LASSO-ELM, Gumbel-Hougaard Copula, 阈值分析

Abstract：

The essential parts of landslide displacement prediction are the improvement of prediction accuracy and threshold computation. Based on the existed research results，an ARMA-LASSO-ELM-Copula model to predict the displacement is constructed in this research. A typical step-like landslide namely Baishuihe landslide is selected for case study in this paper. First，the ACF in ARMA model is selected to investigate the seasonal patterns of monthly precipitation and reservoir water levels. The PPMCC is also selected to analyze the data of triggering factors(e.g.，precipitation and water reservoir levels). The autocorrelation between the current value and the lagged historic values are computed. Second，all positive lagged triggering factors are selected as inputs of the prediction model. An ELM-Copula prediction model is used to predict the monthly increment displacement. The cumulative displacement can be obtained from the accumulation of the predicted increment values. Last，the best performing Copula model is selected based on the triggering factor values and predicted increment displacement. The joint distribution function is constructed to extract the optimal thresholds. Numerical results indicate the LASSO-ELM offers the better prediction accuracy in comparison with SVR，NN，ELM，and etc. The Gumbel-Hougaard Copula function can better describe the joint distribution between triggering factors and predicted displacement values. The threshold of triggering factors and the value-at-risk of displacement can be computed from the selected Copula model.

Key words： slope engineering landslide displacement prediction seasonality pattern ARMA LASSO-ELM Gumbel-Hougaard copula threshold computation

引用本文:

李骅锦1，许强1，何雨森2，朱星1. “阶跃式”滑坡位移预测及阈值分析的ARMA-(LASSO-ELM)-Copula模型[J]. 岩石力学与工程学报, 2017, 36(S2): 4075-4084.
LI Huajin1，XU Qiang1，HE Yusen2，ZHU Xing1. An ARMA-(LASSO-ELM)-Copula framework for landslide displacement prediction and threshold computing of the displacement of step-like landslides. , 2017, 36(S2): 4075-4084.

链接本文:

https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2016.1542 或 https://rockmech.whrsm.ac.cn/CN/Y2017/V36/IS2/4075

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