Skip to main content
SpringerLink
Log in

A semantic information-driven stepwise landslide displacement prediction model

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Landslide prediction is critical for the early warning of a landslide occurrence. Existing stepwise landslide displacement prediction methods are mostly data-driven approaches. However, these models are vulnerable to overfitting, and the low-dimensional numerical features with high numerical volatility prevent them from precisely quantifying the rapid increase in daily displacement in the acceleration phase. Therefore, we propose a semantic information-driven stepwise landslide displacement prediction model comprising an identifier in the displacement phase and a predictor in the acceleration phase. First, the raw landslide monitoring data are converted into text-based semantic information and the semantic features are fused. Subsequently, based on the daily displacement and velocity, we propose a sliding window phase division algorithm to divide the stepwise landslide phase into stationary and acceleration phases. Finally, the landslide displacement phase is identified, and the displacement during the acceleration phase is predicted. The experimental results of the model on the Xinpu and Qingshi landslides in Chongqing, China, show that the proposed model exploits the derived semantic information to identify the landslide acceleration phase qualitatively, and predict the daily displacement of the acceleration phase quantitatively. The proposed model provides a valuable reference for the early warning of stepwise landslides.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Availability of data and material

Not applicable.

References

  • Aggarwal, A., Alshehri, M., Kumar, M., Alfarraj, O., Sharma, P., & Pardasani, K. R. (2020). Landslide data analysis using various time-series forecasting models. Computers & Electrical Engineering, 88, 106858.

    Article  Google Scholar 

  • Cai, Z., Xu, W., Meng, Y., Shi, C., & Wang, R. (2016). Prediction of landslide displacement based on galssvm with multiple factors. Bulletin of Engineering Geology and the Environment, 75(2), 637–646. https://doi.org/10.1007/s10064-015-0804-z

    Article  Google Scholar 

  • Chen, H., & Zeng, Z. (2013). Deformation prediction of landslide based on improved back-propagation neural network. Cognitive Computation, 5(1), 56–62. https://doi.org/10.1007/s12559-012-9148-1

    Article  Google Scholar 

  • Church, K. W. (2017). Word2vec. Natural Language Engineering, 23(1), 155–162.

    Article  Google Scholar 

  • Du, J., Yin, K., & Chai, B. (2009). Study of displacement prediction model of landslide based on response analysis of inducing factors. Chinese Journal of Rock Mechanics and Engineering, 28(9), 1783–1789.

    Google Scholar 

  • Ge, Q., Sun, H., Liu, Z., Yang, B., Lacasse, S., & Nadim, F. (2021). A novel approach for displacement interval forecasting of landslides with step-like displacement pattern. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(9), 1–15. https://doi.org/10.1080/17499518.2021.1892769

  • Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with lstm. Neural Computation, 12(10), 2451–2471. https://doi.org/10.1162/089976600300015015

    Article  CAS  Google Scholar 

  • Goldberg, Y., & Levy, O. (2014). word2vec explained: Deriving mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:14023722

  • Guo, Z., Chen, L., Gui, L., Du, J., Yin, K., & Do, H. M. (2020). Landslide displacement prediction based on variational mode decomposition and wa-gwo-bp model. Landslides, 17(3), 567–583. https://doi.org/10.1007/s10346-019-01314-4

    Article  Google Scholar 

  • Han, H., Shi, B., & Zhang, L. (2021). Prediction of landslide sharp increase displacement by svm with considering hysteresis of groundwater change. Engineering Geology, 280, 105876.

    Article  Google Scholar 

  • Honglie, S. (2000). Climate resources science. Chinese resources science encyclopedia (pp. 468–532). China Petroleum Press.

    Google Scholar 

  • Huber, P. J. (1992). Robust estimation of a location parameter. Breakthroughs in statistics (pp. 492–518). Springer.

    Chapter  Google Scholar 

  • Intrieri, E., Carlà, T., & Gigli, G. (2019). Forecasting the time of failure of landslides at slope-scale: A literature review. Earth-Science Reviews, 193, 333–349. https://doi.org/10.1016/j.earscirev.2019.03.019

    Article  Google Scholar 

  • Jiang, H., Li, Y., Zhou, C., Hong, H., Glade, T., & Yin, K. (2020). Landslide displacement prediction combining lstm and svr algorithms: A case study of shengjibao landslide from the three gorges reservoir area. Applied Sciences, 10(21), 7830. https://doi.org/10.3390/app10217830

    Article  CAS  Google Scholar 

  • Li, L., Wu, Y., Miao, F., Xue, Y., Zhang, L., Liao, K., Teng, W., & Shi, H. (2019). Displacement interval prediction method for step-like landslides considering deformation state dynamic switching. Journal of the Korean Geotechnical Society, 2, 248–248.

    Google Scholar 

  • Li, X., & Kong, J. (2014). Application of ga-svm method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14(3), 525–533.

    Article  Google Scholar 

  • Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988.

  • Liu, Y., Xu, C., Huang, B., Ren, X., Liu, C., Hu, B., & Chen, Z. (2020). Landslide displacement prediction based on multi-source data fusion and sensitivity states. Engineering Geology, 271, 105608. https://doi.org/10.1016/j.enggeo.2020.105608

    Article  Google Scholar 

  • Loshchilov, I., & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983

  • Novaković, J. D., Veljović, A., Ilić, S. S., Papić, Ž, & Milica, T. (2017). Evaluation of classification models in machine learning. Theory and Applications of Mathematics & Computer Science, 7(1), 39–46.

    Google Scholar 

  • Ren, F., Wu, X., Zhang, K., & Niu, R. (2015). Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the shuping landslide in the three gorges, China. Environment and Earth Science, 73(8), 4791–4804. https://doi.org/10.1007/s12665-014-3764-x

    Article  Google Scholar 

  • Sutskeve, I., Martens, J., Dahl, G., & Hinton, G. (2013). On the importance of initialization and momentum in deep learning. International conference on machine learning (pp. 1139–1147). PMLR.

    Google Scholar 

  • Tang, H., Wasowski, J., & Juang, C. H. (2019). Geohazards in the three gorges reservoir area, China-lessons learned from decades of research. Engineering Geology, 261, 105267. https://doi.org/10.1016/j.enggeo.2019.105267

    Article  Google Scholar 

  • Tsai, Y. H. H., Bai, S., Yamada, M., Morency, L. P., & Salakhutdinov, R. (2019). Transformer dissection: A unified understanding of transformer’s attention via the lens of kernel. arXiv preprint arXiv:190811775

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:170603762

  • Wang, J., Wang, Z., Zhang, D., & Yan, J. (2017). Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, vol 350.

  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82.

    Article  Google Scholar 

  • Wu, L., Li, S., Huang, R., & Xu, Q. (2020). A new grey prediction model and its application to predicting landslide displacement. Applied Soft Computing, 95, 106543. https://doi.org/10.1016/j.asoc.2020.106543

    Article  Google Scholar 

  • Wu, X., Chen, X., Zhan, F. B., & Hong, S. (2015). Global research trends in landslides during 1991–2014: A bibliometric analysis. Landslides, 12(6), 1215–1226. https://doi.org/10.1007/s10346-015-0624-z

    Article  Google Scholar 

  • Xu, Q., Peng, D., Zhang, S., Zhu, X., He, C., Qi, X., Zhao, K., Xiu, D., & Ju, N. (2020). Successful implementations of a real-time and intelligent early warning system for loess landslides on the Heifangtai terrace, China. Engineering Geology, 278, 105817. https://doi.org/10.1016/j.enggeo.2020.105817

    Article  Google Scholar 

  • Xu, S., & Niu, R. (2018). Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in three gorges area, China. Computers & Geosciences, 111, 87–96. https://doi.org/10.1016/j.cageo.2017.10.013

    Article  Google Scholar 

  • Yang, B., Yin, K., Lacasse, S., & Liu, Z. (2019). Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides, 16(4), 677–694. https://doi.org/10.1007/s10346-018-01127-x

    Article  Google Scholar 

  • Zhou, C., Yin, K., Cao, Y., Intrieri, E., Ahmed, B., & Catani, F. (2018). Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides, 15(11), 2211–2225. https://doi.org/10.1007/s10346-018-1022-0

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Smart City, Chongqing Jiaotong University, Chongqing, 40074, China

    Feifei Tang, Tianjun Tang & Chuan Hu

  2. National & Local Joint Engineering Laboratory of Transportation and Civil Engineering Materials, Chongqing Jiaotong University, Chongqing, 40074, China

    Hongzhou Zhu

  3. State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, 40074, China

    Haifei Jiang

Authors
  1. Feifei Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianjun Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongzhou Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haifei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feifei Tang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, F., Tang, T., Zhu, H. et al. A semantic information-driven stepwise landslide displacement prediction model. Environ Monit Assess 194, 836 (2022). https://doi.org/10.1007/s10661-022-10417-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10417-w

Keywords

Search

Navigation