【摘要】：黃土滑坡的變形演化過程往往受到多種因素的影響,呈現(xiàn)出非線性特征�；谛〔ǚ治龊瘮�(shù)(Wavelet Analysis,WA)、提升回歸樹(Boosting Regression Tree,BT),以及極限訓練機(Extreme Learning Machine,ELM)方法,提出一種名為WA-BT-ELM的黃土滑坡位移預測新方法。該方法將非線性位移數(shù)據(jù)作為一時間序列,運用小波分析函數(shù)將監(jiān)測點累積位移曲線分解為若干子小波;隨后使用提升回歸樹對所有子小波進行重要度分析,剔除相關(guān)性不高的子小波以去掉冗雜信息;最后運用極限訓練機,結(jié)合篩選得到的子小波對滑坡位移進行預測分析�；谠撃Ｐ蛯Ω拭C省永靖縣黑方臺滑坡區(qū)的滑坡位移監(jiān)測數(shù)據(jù)進行預測,得到了優(yōu)于ANN,BPNN,SVM,ELM,以及WAELM預測模型的結(jié)果,故認為WA-BT-ELM模型是一種有效的黃土滑坡位移預測方法。
[Abstract]:The deformation and evolution process of loess landslide is often influenced by many factors, showing nonlinear characteristics. Based on wavelet analysis function (Wavelet Analysis,WA), lifting regression tree (Boosting Regression Tree,BT) and limit training machine (Extreme Learning Machine,ELM), a new method called WA-BT-ELM for predicting loess landslide displacement is proposed. In this method, the nonlinear displacement data is taken as a time series, and the cumulative displacement curve of monitoring points is decomposed into several subwavelets by wavelet analysis function, and then the importance of all subwavelets is analyzed by lifting regression tree. In order to remove the miscellaneous information, the subwavelet with low correlation is eliminated. Finally, the landslide displacement is predicted and analyzed by using the limit training machine and the selected subwavelet. Based on this model, the monitoring data of landslide displacement in Heifangtai landslide area of Yongjing County, Gansu Province are predicted, and the results are better than those of ANN,BPNN,SVM,ELM, and WAELM model. It is considered that WA-BT-ELM model is an effective method for prediction of loess landslide displacement.
【作者單位】： 成都理工大學地質(zhì)災害防治與地質(zhì)環(huán)境保護國家重點實驗室;山東大學(威海)數(shù)學與統(tǒng)計學院;愛荷華大學智能系統(tǒng)研究實驗室;
【基金】：國家重點基礎研究計劃(973計劃)資助項目(2014CB744703) 國家杰出青年科學基金項目(41225011) 教育部“長江學者獎勵計劃”項目(T2011186)
【分類號】：P642.22

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