Abstract

Many models have been developed for landslide displacement prediction, but owing to complex landslide-formation mechanisms and landslide-inducing factors, such models have different prediction accuracies. Thus, landslide displacement prediction remains a popular but difficult topic of research. In this paper, a landslide prediction model is proposed by combining extreme learning machine (ELM) and random search support vector regression (RS-SVR) sub-models. Particularly, the combined model decomposed accumulative landslide displacement into two terms, trend and periodic displacements, using a time series model, and simulated and predicted the two terms using the ELM and RS-SVR sub-models, respectively. The predicted trend and periodic terms are then summed to obtain the total displacement. The ELM and RS-SVR sub-models are applied to predict the deformation of Baishuihe landslide in the Three Gorges Reservoir Area (TGRA) as an example. The results showed that the model effectively improved the accuracy, stability, and scope of application of landslide displacement prediction, thus providing a new method for landslide displacement prediction.

摘要

目前已有多种滑坡位移预测模型，但由于滑坡形成机制和滑坡诱发因素复杂，这些模型的预测精度各不相同。因此，滑坡位移预测仍然是一个热门但困难的研究课题。在本文中，通过结合极限学习机（ELM）和随机搜索支持向量回归（RS-SVR）子模型提出了滑坡预测模型。特别地，组合模型使用时间序列模型将累积滑坡位移分解为趋势位移和周期位移两项，并分别使用ELM和RS-SVR子模型对这两项进行模拟和预测。然后将预测的趋势项和周期项相加以获得总位移。以三峡库区（TGRA）白水河滑坡变形预测应用ELM和RS-SVR子模型为例。结果表明，该模型有效提高了滑坡位移预测的精度、稳定性和适用范围，为滑坡位移预测提供了一种新方法。