Abstract：To solve the problem that the traditional landslide displacement point prediction model cannot effectively describe the reliability of the prediction result，a landslide displacement interval prediction model based on different Bootstrap methods and KELM-BPNN model was proposed by introducing the method of interval prediction. In this model，firstly，the original dataset consisting of the monitoring information from various external trigger factors and landslide surface displacement was randomly sampled with an equal probability for B times and then，B dummy datasets were obtained based on different Bootstrap processes. B KELM models were trained to estimate the variance of the system error respectively and consequently，a BPNN model was trained to regress the variance of the random error. Finally，the variances of the system error and the random error，both obtained from the same Bootstrap process，were combined to construct the landslide displacement prediction intervals with different confidence levels. Through comparisons，an optimal displacement interval prediction model fitting in the deformation characteristics of actual landslides was proposed. Baishuihe Landslide，a typical colluvial landslide with step-like behaviour in the area of Three Gorges Reservoir，was taken as an example. The monitoring data of ZG93 and ZG118 from July 2004 to December 2013 were analysed. The results show that, compared with the traditional point prediction model，the developed model not only provides a relatively accurate point prediction result but also constructs a clear and reliable displacement prediction interval to cover the landslide displacement curve completely. In addition，the dynamic variation of the prediction interval width can be used to better quantify and explain the uncertain impact of the dynamic change of external triggering factors on the landslide evolution，which offers a new idea or option for the forecasting and the early warning of landslides.
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2019, Vol. 38 
