In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

中文翻译：

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GWO-ELM模型在三峡库区曹家沱滑坡位移预测中的应用

为了建立有效的滑坡灾害预警系统，准确的滑坡位移预测是核心。本文选取三峡水库（TGR）地区典型的阶梯式滑坡（曹家沱滑坡），提出一种基于灰狼优化的极限学习机位移预测模型（GWO-ELM模型）。通过对滑坡位移监测数据的分析，采用移动平均法将滑坡位移时间序列分解为趋势位移和周期位移。首先，趋势位移由三次多项式拟合，采用稳健的加权最小二乘法。然后，结合内部演化规律和外部影响因素，得出周期性位移的主要外部触发因素是库区降水和水位的变化。采用灰色关联度(GRG)分析方法筛选出滑坡周期位移的主要影响因素。以这些因素为输入项，利用GWO-ELM模型预测滑坡的周期性位移。将结果与未优化的 ELM 模型进行比较。结果表明，结合GWO算法调整参数少、全局搜索能力强等优点，GWO-ELM模型能够有效地学习数据的变化特征，具有较好且相对稳定的预测精度。采用灰色关联度(GRG)分析方法筛选出滑坡周期位移的主要影响因素。以这些因素为输入项，利用GWO-ELM模型预测滑坡的周期性位移。将结果与未优化的 ELM 模型进行比较。结果表明，结合GWO算法调整参数少、全局搜索能力强等优点，GWO-ELM模型能够有效地学习数据的变化特征，具有较好且相对稳定的预测精度。采用灰色关联度(GRG)分析方法筛选出滑坡周期位移的主要影响因素。以这些因素为输入项，利用GWO-ELM模型预测滑坡的周期性位移。将结果与未优化的 ELM 模型进行比较。结果表明，结合GWO算法调整参数少、全局搜索能力强等优点，GWO-ELM模型能够有效地学习数据的变化特征，具有较好且相对稳定的预测精度。