Abstract
Predicting rainfall-induced landslide displacement is one of the important means of disaster prevention and mitigation. Considering the Tanjiawan landslide in the Three Gorges Reservoir area as the research object, the daily rainfall and soil moisture content as influencing factors, complementary ensemble empirical mode decomposition (CEEMD) was used to decompose the time series of displacement and influencing factors, followed by K-means clustering to determine the periodic displacement, random displacement, trend displacement, and their corresponding influencing factor components after decomposition. The Grey System theory was used to test the correlation between the influencing factor and decomposition displacement, and the least squares support vector machine based on particle swarm optimization (PSO-LSSVM) and the least square method were used to predict the decomposition displacement. The results showed that after decomposition and clustering, the grey relational degree between the influencing factor and the decomposition displacement is up to 0.91, which showed that the selection of the displacement decomposition and the influencing factor is reliable. A coefficient of determination of 1.00 indicated that the quadratic least squares function model can predict the trend displacement well, and the root mean squared error value of the PSO-LSSVM model predicting displacement did not exceed 21.62 mm. At the same time, compared with the prediction results without considering water content as the influencing factor, the results show that the prediction effect considering water content as the influencing factor is very reliable, and the model in this study can achieve the displacement prediction of rainfall-type landslides satisfactorily.
Similar content being viewed by others
References
Bernardie S, Desramaut N, Malet JP et al (2015) Prediction of changes in landslide rates induced by rainfall. Landslides 12(3):481–494. https://doi.org/10.1007/s10346-014-0495-8
Cai ZL, Xu WY, Meng YD et al (2016) Prediction of landslide displacement based on GA-LSSVM with multiple factors. Bull Eng Geol Environ 75(2):637–646. https://doi.org/10.1007/s10064-015-0804-z
Calvello M, Cascini L, Sorbino G (2008) A numerical procedure for predicting rainfall-induced movements of active landslides along pre-existing slip surfaces. Int J Numer Anal Methods Geomech 32(4):327–351. https://doi.org/10.1002/nag.624
Cao Y, Yin KL, Alexander DE et al (2016) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13(4):725–736. https://doi.org/10.1007/s10346-015-0596-z
Deng JL (1982) The grey control system. J Huazhong Inst Technol 10(3):9–18. https://doi.org/10.13245/j.hust.1982.03.002 (In Chinese)
Deng DM, Liang Y, Wang LQ et al (2017) Displacement prediction method based on ensemble empirical mode decomposition and support vector machine regression—a case of landslides in Three Gorges Reservoir area. Rock Soil Mech 38(12):3660–3669. https://doi.org/10.16285/j.rsm.2017.12.034 (In Chinese)
Espinoza M, Suykens JAK, Moor BD (2003) Least squares support vector machines and primal space estimation. In: Proceedings of the IEEE 42nd conference on decision and control, vol 4. pp 3451–3456. https://doi.org/10.1109/CDC.2003.1271680
Finlay PJ, Fell R, Maguire PK (1996) The relationship between the probability of landslide ccurrence and rainfall Canadian. Geotech J 34(6):811–824. https://doi.org/10.1139/t97-047
Gao HX, Yin KL (2007) Discuss on the correlations between landslides and rainfall and threshold for landslide early warning and prediction. Rock Soil Mech 28(5):1055–1060. https://doi.org/10.16285/j.rsm.2007.05.039 (in Chinese)
Helmstetter A, Sornette D, Andersen JV (2004) Slider block friction model for landslides: application to Vaiont and Laclapière landslides. J Geophys Res. https://doi.org/10.1029/2002JB002160
Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. R Soc. https://doi.org/10.1098/rspa.1998.0193
Huang FM, Huang JS, Jiang SH et al (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186. https://doi.org/10.1016/j.enggeo.2017.01.016
Huang XH, Guo F, Deng ML et al (2020) Understanding the deformation mechanism and threshold reservoir level of the floating weight-reducing landslide in the Three Gorges Reservoir area. China Landslides 17(12):2879–2894. https://doi.org/10.1007/s10346-020-01435-1
Jibson RW, Harp EL, Michael JA (2000) A method for producing digital probabilistic seismic landslide hazard maps. Eng Geol 58(3–4):271–289. https://doi.org/10.1016/s0013-7952(00)00039-9
Li Y, Meng H, Dong Y et al (2004) Main types and characteristics of geo-hazard in China-Based on the results of geo-hazard survey in 290 counties. Chin J Geol Hazard Control 15(2):29–34. https://doi.org/10.3969/j.issn.1003-8035.2004.02.005 (In Chinese)
Li LW, Wu YP, Miao FS et al (2018) Displacement prediction of landslide based on variational mode decomposition and GWO-MIC-SVR model. Chin J Rock Mech Eng 37(6):1395–1406. https://doi.org/10.13722/j.cnki.jrme.2017.1508 (In Chinese)
Li SH, Wu LZ, Huang JS (2021a) A novel mathematical model for predicting landslide displacement. Soft Comput 25:2453–2466. https://doi.org/10.1007/s00500-020-05313-9
Li LW, Wu YP, Miao FS et al (2021b) A hybrid interval displacement forecasting model for reservoir colluvial landslides with step-like deformation characteristics considering dynamic switching of deformation states. Stoch Environ Res Risk Assess 35(6):1089–1112. https://doi.org/10.1007/s00477-020-01914-w
Saito M (1965) Forecasting the time of occurrence of slope failure. In: Proceedings of the Sixth ICSMFE, Montreal. vol 2. pp 537–541
Shi YH, Eberhart RC (1998) Parameter selection in particle swarm optimization. Evolutionary programming 1998 computer science. https://doi.org/10.1007/BFb0040810
Suykens JAK, Lukas L, Van P (1999) Least squares support vector machine classiers: a large scale algorithm. In: European conference on circuit theory and design, vol 1999, pp 839–842
Voight B (1989) A relation to describe rate-dependent material failure. Science 243(4888):200–203. https://doi.org/10.1126/science.243.4888.200
Wang L, Chen YS, Wang SM et al (2022) Response of landslide deformation to rainfall based on multi-index monitoring: a case of the Tanjiawan landslide in the Three Gorges Reservoir. Bull Eng Geol Environ 81:231. https://doi.org/10.1007/s10064-022-02732-w
Wang L, Wang SM, Li G et al (2020) Construction of 3D creep model of landslide slip-surface soil and secondary development based on FLAC3D. Adv Civ Eng 2020:2694651. https://doi.org/10.1155/2020/2694651
Xu SL, Niu RQ (2018) Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Comput Geosci 111:87–96. https://doi.org/10.1016/j.cageo.2017.10.013
Yin KL, Yan TZ (1996) Landslide prediction and relevant models. Rock Mech Eng 15(1):1–8 (In Chinese)
Yang BB, Yin KL, Du J (2018) A model forpredicting landslide displacement based on time series and long and short term memory neural network. Chin J Rock Mech Eng 37(10):2334–2343 (In Chinese)
Yeh JR, Shieh JS, Norden H (2010) Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv Adapt Data Anal 2(2):135–156. https://doi.org/10.1142/S1793536910000422
Zhang P, Dai YS, Zhang HQ et al (2019) Combining CEEMD and recursive least square for the extraction of time-varying seismic wavelets. J Appl Geophys 170:103854. https://doi.org/10.1016/j.jappgeo.2019.103854
Zhang YG, Chen XQ, Liao RP et al (2021) Research on displacement prediction of step-type landslide under the influence of various environmental factors based on intelligent WCA-ELM in the Three Gorges Reservoir area. Nat Hazards 107(2):1709–1729. https://doi.org/10.1007/s11069-021-04655-3
Zhang YG, Tang J, Cheng Y et al (2022) Prediction of landslide displacement with dynamic features using intelligent approaches. Int J Min Sci Technol. https://doi.org/10.1016/j.ijmst.2022.02.004
Zhao NH, Hu B, Yan E et al (2019) Research on the creep mechanism of Huangniba landslide in the three gorges reservoir area of China considering the seepage-stress coupling effect. Bull Eng Geol Environ 78(6):4107–4121. https://doi.org/10.1007/s10064-018-1377-4
Zhou C, Yin KL, Cao Y et al (2016) Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng Geol 204:108–120. https://doi.org/10.1016/j.enggeo.2016.02.009
Acknowledgements
This work was supported by National Natural Science Foundation Key Projects of China (No. U21A2031) and China Postdoctoral Science Foundation (2021M701969). A part of the data for the manuscript was collected with the assistance of the Yichang Geological Environment Monitoring and Protection Station.
Funding
Funding was provided by National Natural Science Foundation Key Projects of China (No. U21A2031) and China Postdoctoral Science Foundation (2021M701969).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare there is no conflict of interest regarding the publication of this paper.
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.
About this article
Cite this article
Wang, L., Chen, Y., Huang, X. et al. Displacement prediction method of rainfall-induced landslide considering multiple influencing factors. Nat Hazards 115, 1051–1069 (2023). https://doi.org/10.1007/s11069-022-05620-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-022-05620-4