Hydrodynamic landslide displacement prediction using combined extreme learning machine and random search support vector regression model

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.

Automated summary

In this paper, a landslide prediction model is proposed by combining ELM and RS-SVR sub-models. The model decomposes the accumulated displacement into trend and periodic displacements and uses ELM and RS-SVR to predict these terms. Application of the model to a case study showed improved accuracy, stability, and scope of landslide displacement prediction.