Time series analysis and long short-term memory neural network to predict landslide displacement
- 378 Downloads
A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory (LSTM) neural network. The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall, and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.
KeywordsDisplacement prediction Step-wise landslide Time series Long short-term memory neural network Three Gorges Reservoir
The authors wish to thank Dr. Du Juan, Dr. Huang Faming, Dr. Miao Fasheng, and Dr. Zhou Chao for their assistance in collecting the data.
This research was supported by the National Natural Sciences Foundation of China (no. 41572292) and the Research Council of Norway (Klima 2050). The first author wishes to thank the China Scholarship Council (CSC) and the Norwegian Geotechnical Institute (NGI) for funding her research period at NGI.
- Chen S, Chou W (2012) Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach. The 15th International IEEE Conference on Intelligent, pp. 1821–1826Google Scholar
- China Institute of Geo-Environment Monitoring (2017) Bulletin of geologic hazards from January to December in 2016. China Institute of Geo-Environment Monitoring, BeijingGoogle Scholar
- Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
- Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, IEEE, pp 39–43Google Scholar
- Fan Y, Qian Y, Xie F, Soong FK (2014) TTS synthesis with bidirectional LSTM based recurrent neural networks. Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH):1964–1968Google Scholar
- Felix A, Jürgen S (2000) Recurrent nets that time and count. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp 3:189–194Google Scholar
- Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. Proceedings of the International Conference on Acoustics, Speech and Signal Processing Acoustics, pp. 6645–6649Google Scholar
- Haque U, Blum P, da Silva PF, Andersen P, Pilz J, Chalov SR, Malet JP, Auflič MJ, Andres N, Poyiadji E, Lamas PC, Zhang W, Peshevski I, Pétursson HG, Kurt T, Dobrev N, García-Davalillo JC, Halkia M, Ferri S, Gaprindashvili G, Engström J, Keellings D (2016) Fatal landslides in Europe. Landslides 13(6):1545–1554CrossRefGoogle Scholar
- Hasim S, Andrew S, Francoise B (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 338–342Google Scholar
- Hsu C, Chang C, Lin C (2010). A practical guide to support vector classification. Technical Report, National Taiwan UniversityGoogle Scholar
- Reimers N, Gurevych I (2017). Optimal hyperparameters for deep LSTM-networks for sequence labeling tasks. arXiv preprint arXiv:1707.06799Google Scholar
- Saito M (1965) Forecasting the time of occurrence of a slope failure. In: Proceedings of the 6th International Mechanics and Foundation Engineering pp: 537–541Google Scholar
- Vincent P, Larochelle H, Lajoie I et al (2010) Stacked denoising autoencoders: learning useful representation in a deep network with a local denoising criterion. J Mach Learn Res 11(12):3371–3408Google Scholar