Abstract：To overcome the drawbacks of previous displacement prediction models for step-like landslides such as the hysteresis and the unclear reliability of the prediction results of mutational displacements，a new hybrid prediction method of landslide displacement intervals is proposed. Firstly，the deformation of step-like landslides is divided into steady and mutational states by the combination of SOM network and K-means clustering. Secondly，on the basis of expanding the mutational state samples through the comprehensive application of the engineering geology analogy method and the adaptive synthetic sampling algorithm，an ensemble classifier for recognizing the landslide deformation states automatically is established adopting the random forest algorithm. Finally，an interval prediction framework considering the dynamic switching of landslide deformation states is constructed based on the Bootstrap-KELM-BPNN model. Taking Baishuihe landslide，a typical step-like landslide in the Three Gorges reservoir area，as an example，the dataset of XD01 monitoring point from June，2006 to December，2016 is explored to verify the effectiveness，accuracy and reliability of the proposed method.
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2019, Vol. 38 
