WA-BT-ELM耦合模型在黄土滑坡位移预测中的应用  

Application of a Novel Predictive Model Integrating Wavelet Analysis,Boosting Regression Tree and Extreme Learning Machine to Loess Landslide Displacement

作　　者：李骅锦[1] 许强[1] 王思澄 亓星[1] 彭大雷[1] 何雨森[3] 

机构地区：[1]成都理工大学地质灾害防治与地质环境保护国家重点实验室,成都610059 [2]山东大学(威海)数学与统计学院,山东威海264209 [3]爱荷华大学智能系统研究实验室

出　　处：《长江科学院院报》2017年第9期63-69,共7页Journal of Changjiang River Scientific Research Institute

基　　金：国家重点基础研究计划(973计划)资助项目(2014CB744703);国家杰出青年科学基金项目(41225011);教育部"长江学者奖励计划"项目(T2011186)

摘　　要：黄土滑坡的变形演化过程往往受到多种因素的影响,呈现出非线性特征。基于小波分析函数(Wavelet Analysis,WA)、提升回归树(Boosting Regression Tree,BT),以及极限训练机(Extreme Learning Machine,ELM)方法,提出一种名为WA-BT-ELM的黄土滑坡位移预测新方法。该方法将非线性位移数据作为一时间序列,运用小波分析函数将监测点累积位移曲线分解为若干子小波;随后使用提升回归树对所有子小波进行重要度分析,剔除相关性不高的子小波以去掉冗杂信息;最后运用极限训练机,结合筛选得到的子小波对滑坡位移进行预测分析。基于该模型对甘肃省永靖县黑方台滑坡区的滑坡位移监测数据进行预测,得到了优于ANN,BPNN,SVM,ELM,以及WAELM预测模型的结果,故认为WA-BT-ELM模型是一种有效的黄土滑坡位移预测方法。The deformation evolution process of loess landslide is often nonlinear due to many factors. A theoretical approach named WA-BT-ELM, which is based on wavelet analysis （WA）, boosting regression tree （BT） and ex- treme learning machine （ ELM）, is proposed to predict loess landslide displacements. By analysis of nonlinear loess landslide time-dependent displacement dataset, the accumulation displacement data signal is decomposed into a se- ries of sub-wavelets. Then, the importance of all the sub-wavelets to the displacement data series is computed by BT algorithm. The highly important sub-wavelets are selected to make further predictions. Furthermore, the predictive results of sub-wavelet and the original landslide displacement series are obtained through ELM algorithms. A case study of Heifangtai landslide in Gansu Province is presented to verify the predictive results. In comparison, the pre- dictive results by using WA-BT-ELM model is faster and more accurate than those by ANN, BPNN, SVM, ELM and WA-ELM model, indicating that the WA-BT-ELM model is effective in loess landslide displacement prediction cases.

关 键 词：黄土滑坡 WA-BT-ELM耦合模型 位移预测 小波变换 提升回归树 极限训练机 

分 类 号：P642.22[天文地球—工程地质学]