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Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model

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Abstract

Landslide prediction is important for mitigating geohazards but is very challenging. In landslide evolution, displacement depends on the local geological conditions and variations in the controlling factors. Such factors have led to the “step-like” deformation of landslides in the Three Gorges Reservoir area of China. Based on displacement monitoring data and the deformation characteristics of the Baishuihe Landslide, an additive time series model was established for landslide displacement prediction. In the model, cumulative displacement was divided into three parts: trend, periodic, and random terms. These terms reflect internal factors (geological environmental, gravity, etc.), external factors (rainfall, reservoir water level, etc.), and random factors (uncertainties). After statistically analyzing the displacement data, a cubic polynomial model was proposed to predict the trend term of displacement. Then, multiple algorithms were used to determine the optimal support vector regression (SVR) model and train and predict the periodic term. The results showed that the landslide displacement values predicted based on data time series and the genetic algorithm (GA-SVR) model are better than those based on grid search (GS-SVR) and particle swarm optimization (PSO-SVR) models. Finally, the random term was accurately predicted by GA-SVR. Therefore, the coupled model based on temporal data series and GA-SVR can be used to predict landslide displacement. Additionally, the GA-SVR model has broad application potential in the prediction of landslide displacement with “step-like” behavior.

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References

  • Bernardie S, Desramaut N, Malet JP et al (2015) Prediction of changes in landslide rates induced by rainfall. Landslides 12(3):481–494

    Article  Google Scholar 

  • Bordoni M et al (2015) Hydrological factors affecting rainfall-induced shallow landslides: from the field monitoring to a simplified slope stability analysis. Eng Geol 193:19–37

    Article  Google Scholar 

  • Bui DT, Tuan TA, Klempe H et al (2016b) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Article  Google Scholar 

  • Bui DT, Tuan TA, Hoang ND, et al. (2016a) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 1-12

  • Bui DT, Tuan TA, Klempe H et al (2016c) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Article  Google Scholar 

  • Carlà T, Intrieri E, Traglia FD et al (2016) A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series. Nat Hazards 84(1):669–683

    Article  Google Scholar 

  • Carlà T, Intrieri E, Farina P et al (2017) A new method to identify impending failure in rock slopes. Int J Rock Mech Mining Sci 93:76–81

    Article  Google Scholar 

  • Cao Y, Yin K, Alexander DE et al (2015) Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13(4):725–736

    Article  Google Scholar 

  • Chousianitis K, Gaudio V, Kalogeras I et al (2014) Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece. Soil Dyn Earthq Eng 65:11–19

    Article  Google Scholar 

  • Crosta GB, Agliardi F (2003) Failure forecast for large rock slides by surface displacement measure. Can Geotech J 40(1):176–191(16)

    Article  Google Scholar 

  • Crosta GB, Agliardi F (2012) How to obtain alert velocity thresholds for large rockslides. Phys Chem Earth 27(36):1557–1565

    Article  Google Scholar 

  • Deng J (1988) Grey forecasting and decision making. Huazhong University of Science and Technology Press, Wuhan, pp 86–128

    Google Scholar 

  • Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides, Three Gorges Reservoir, China. Landslides 10(2):203–218

    Article  Google Scholar 

  • Du W, Wang G (2016) A one-step Newmark displacement model for probabilistic seismic slope displacement hazard analysis. Eng Geol 205:12–23

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, IEEE, pp 39–43

    Google Scholar 

  • Faculty of civil and construction, Three Gorges University (2013) Emergency management survey report of the Baishuihe Landslide, subsequent geological disaster prevention and control of Zigui County project in the Three Gorges Reservoir area. Three Gorges University, Hubei province

    Google Scholar 

  • Federico A, Popescu M, Elia G et al (2012) Prediction of time to slope failure: a general framework. Environ Earth Sci 66(1):245–256

    Article  Google Scholar 

  • Fukuzono T (1985) A new method for predicting the failure time of a slope. Proceedings of the 4th International Conference and Field Workshop on Landslides, Tokyo. University Press, Tokyo, pp 145–150

    Google Scholar 

  • Gao X, Hou J (2016) An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing 174:906–911

    Article  Google Scholar 

  • Hayashi S, Komamura F, Park B (1988) On the forecast of time to failure of slope–time process of slope failure. J Jpn Landslide Soc 24(4):11–18

    Article  Google Scholar 

  • Hong H, Pradhan B, Xu C et al (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281

    Article  Google Scholar 

  • Hong H, Pourghasemi HR, Pourtaghi ZS (2016a) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118

    Article  Google Scholar 

  • Hong H, Pradhan B, Bui DT, et al. (2016b) Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Natural Hazards & Risk

  • Hong H, Pradhan B, Jebur MN et al (2016c) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75(1):1–14

    Article  Google Scholar 

  • Huang R, Xu Q (1997) Collaborative forecasting model of slope instability time. Mt Res 01:7–12

    Google Scholar 

  • Hwang G, Chen C (2013) A study of the Newmark sliding block displacement functions. Bull Earthq Eng 11:481–502

    Article  Google Scholar 

  • Jebur MN, Pradhan B, Tehrany MS (2015) Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl Earth Obs Remote Sens 8(2):674–690

    Article  Google Scholar 

  • Jiao Y, Zhang H, Tang H et al (2014) Simulating the process of reservoir-impoundment-induced landslide using the extended DDA method. Eng Geol 182:37–48

    Article  Google Scholar 

  • Li D, Yin K, Leo C (2010) Analysis of Baishuihe landslide influenced by the effects of reservoir water and rainfall. Environ Earth Sci 60(4):677–687

    Article  Google Scholar 

  • Li X, Zhang N, Liao Q et al (2004) Analysis of groundwater dynamic field under the combined action of reservoir water level fluctuation and rainfall. Chin J Rock Mech Eng 21:3714–3720

    Google Scholar 

  • Lian C, Zeng Z, Yao W et al (2015) Multiple neural networks switched prediction for landslide displacement. Eng Geol 186:91–99

    Article  Google Scholar 

  • Liu X, Liu G, Chen W et al (2003) Comprehensive analysis of the impact of rainfall on slope deformation and failure. Chin J Rock Mech Eng S2:2715–2718

    Google Scholar 

  • Liu Z, Shao J, Xu W et al (2014) Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides 11:889–896

    Article  Google Scholar 

  • Mufundirwa A, Fujii Y, Kodama J (2010) A new practical method for prediction of geomechanical failuretime. Int J Rock Mech Min Sci 47(7):1079–1090

    Article  Google Scholar 

  • Newcomen W, Dick G (2016) An update to the strain-based approach to pit wall failure prediction, and a justification for slope monitoring. J South Afr Inst Min Metall 116(5):379–385

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-flizzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Qin S (2005) Nonlinear evolution mechanism and physical prediction of slope instability process. Chin J Rock Mech Eng 11:6–13

    Google Scholar 

  • Saito M (1965) Forecasting the time of occurrence of a slope failure. In: Proceedings of the 6th International Mechanics and Foundation Engineering, Montr al, Que. Pergamon Press, Oxford 537-541

  • Segoni S, Lagomarsino D, Fanti R et al (2015) Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system. Landslides 12(4):773–785

    Article  Google Scholar 

  • Taskin K, Emrehan KS, Ismail C (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112

    Article  Google Scholar 

  • Tomas R, Li Z, Liu P (2014) Spatiotemporal characteristics of the Huangtupo landslide in the Three Gorges region (China) constrained by radar interferometry. Geophys J Int 197(1):213–232

    Article  Google Scholar 

  • Vapnik V (2000) The nature of statistical learning theory. Springer-Verlag, New York USA, pp 138–167

    Book  Google Scholar 

  • Voight B (1989) A relation to describe rate-dependent material failure. Science 243:200–203

    Article  Google Scholar 

  • Wang Y, Yin K, An G (2004) Grey correlation analysis of landslide sensitive factor. Rock Soil Mech 01:91–93

    Google Scholar 

  • Yang S (1992) Engineering application of time series analysis. Huazhong University of Science and Technology Press, Wuhan

    Google Scholar 

  • Yao W, Zeng Z, Lian C et al (2015) Training enhanced reservoir computing predictor for landslide displacement. Eng Geol 188:101–109

    Article  Google Scholar 

  • Yin K, Yan T (1996) Landslide prediction and related models. Chin J Rock Mech Eng 01:1–8

    Google Scholar 

  • Zhou C, Yin K, 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

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 41272307 and No. 41572278). We thank the colleagues in our laboratory for their constructive comments and assistance.

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Authors and Affiliations

  1. Engineering Faculty, China University of Geosciences, Wuhan, 430074, China

    Fasheng Miao, Yiping Wu, Yuanhua Xie & Yaonan Li

  2. Three Gorges Research Center for Geo-hazard, Wuhan, 430074, China

    Yiping Wu

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  1. Fasheng Miao
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  2. Yiping Wu
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  3. Yuanhua Xie
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  4. Yaonan Li
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Correspondence to Yiping Wu.

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Miao, F., Wu, Y., Xie, Y. et al. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides 15, 475–488 (2018). https://doi.org/10.1007/s10346-017-0883-y

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  • DOI: https://doi.org/10.1007/s10346-017-0883-y

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