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

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.

Keywords:

• hydrodynamic landslide
• extreme learning machine
• support vector regression
• random search
• displacement prediction

Acknowledgements

We thank the National Field Observation and Research Station of Landslides in the TGRA of Yangtze River for their help in providing monitoring data for this study.

Additional information

Funding

This work is supported by the National Key Research and Development Program of China (No. 2017YFC1501100), the Open Foundation of National Field Observation and Research Station of Landslides in the TGRA of Yangtze River, China Three Gorges University (2018KTL03), the Fundamental Research Funds for the Central Universities (No. 2019B13814), the Open Foundation of Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University (No. GHXN201905).