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Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

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Abstract

Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations (i.e., periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the wavelet transform (WT) and particle swarm optimization-kernel extreme learning machine (PSO-KELM) methods, and by considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with that of the multi-factor extreme learning machine (ELM), support vector regression (SVR), backward propagation neural network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms the other models, and the prediction accuracy can be improved by considering causal factors.

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References

  • An H, Viet TT, Lee G, Kim Y, Kim M, Noh S, Noh J (2016) Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow. Environ Model Softw 85:172–183

    Article  Google Scholar 

  • Barzegar R, Adamowski J, Moghaddam AA (2016) Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch Env Res Risk A 30(7):1797–1819

    Article  Google Scholar 

  • Bogaard T, Greco R (2018) Invited perspectives: hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds. Nat Hazard Earth Sys 18(1):31–39

    Article  Google Scholar 

  • Cao Y, Yin K, Alexander DE, Zhou C (2016) 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 

  • Cao Y, Yin K, Zhou C (2013) Comprehensive assessment on Sanzhouxi landslide stability considering displacement monitoring. Electr J Geol Eng 18:5507–5524

    Google Scholar 

  • Carlà T, Intrieri E, Di Traglia F, Casagli N (2016) A statistical-based approach for establishing probabilistic warning thresholds of flank eruption occurrence using one-step ahead forecasts of displacement time series. Nat Hazards 84(1):669–683

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Casagli N, Catani F, Del Ventisette C, Luzi G (2010) Monitoring, prediction, and early warning using ground-based radar interferometry. Landslides 7(3):291–301

    Article  Google Scholar 

  • Chen Q, Kou X, Huang S, Zhou Y (2004) The distributes and geologic environment characteristics of red beds in China. J Eng Geol 12(1):34–40

    Google Scholar 

  • Conte E, Donato A, Troncone A (2017) A simplified method for predicting rainfall-induced mobility of active landslides. Landslides 14(1):35–45

    Article  Google Scholar 

  • Corominas J, Moya J, Ledesma A, Lloret A, Gili JA (2005) Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides 2(2):83–96

    Article  Google Scholar 

  • Crosta GB, Agliardi F (2012) How to obtain alert velocity thresholds for large rockslides. Physics and Chemistry of the Earth 27(36):41):1557–1565

    Article  Google Scholar 

  • Crozier MJ (1986) Landslides: causes, consequences & environment. Taylor & Francis

  • Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE T Inform Theory 36(5):961–1005

    Article  Google Scholar 

  • Daubechies I (1992) Ten lectures on wavelets. Society for industrial and applied mathematics

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

    Article  Google Scholar 

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, pp. 39–43

  • Eid HT (2014) Stability charts for uniform slopes in soils with nonlinear failure envelopes. Eng Geol 168:38–45

    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 in Landslides Tokyo

  • Glade T, Anderson M, Crozier MJ (2005) Landslide hazard and risk: issues, concepts and approach. In: Landslide hazard and risk. Wiley, pp 1–40

  • Haar A (1910) Zur theorie der orthogonalen funktionensysteme. Math Ann 69(3):331–371

    Article  Google Scholar 

  • Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186

    Article  Google Scholar 

  • Huang G, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 42(2):513–529

    Article  Google Scholar 

  • Huang G, Zhu Q, Siew C (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Neural networks 2004 proceedings pp: 985–990

  • Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  • Intrieri E, Gigli G (2016) Landslide forecasting and factors influencing predictability. Nat Hazards Earth Syst Sci 16(12):2501–2510

    Article  Google Scholar 

  • Intrieri E, Gigli G, Casagli N, Nadim F (2013) Brief communication: landslide early warning system: toolbox and general concepts. Nat Hazards Earth Syst Sci 13:85–90

    Article  Google Scholar 

  • Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown WM III, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS (1987) Real-time landslide warning during heavy rainfall. Science 238(4829):921–926

    Article  Google Scholar 

  • Krkač M, Špoljarić D, Bernat S, Arbanas SM (2017) Method for prediction of landslide movements based on random forests. Landslides 14(3):947–960

    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 

  • Lian C, Zeng Z, Yao W, Tang H (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch Env Res Risk A 28:1957–1972

    Article  Google Scholar 

  • Lima AR, Cannon AJ, Hsieh WW (2015) Nonlinear regression in environmental sciences using extreme learning machines: a comparative evaluation. Environ Model Softw 73:175–188

    Article  Google Scholar 

  • Liu Y, Liu D, Qin Z, Liu F, Liu L (2016) Rainfall data feature extraction and its verification in displacement prediction of Baishuihe landslide in China. B Eng Geol Environ 75(3):897–907

    Article  Google Scholar 

  • Ma J, Tang H, Liu X, Hu X, Sun M, Song Y (2017) Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China. Landslides 14(3):1275–12281

    Article  Google Scholar 

  • Mallat SG (1989) A theory for multi-resolution signal decomposition: the wavelet representation. IEEE T Pattern Anal 11(7):674–693

    Article  Google Scholar 

  • Mazzanti P, Bozzano F, Cipriani I, Prestininzi A (2015) New insights into the temporal prediction of landslides by a terrestrial SAR interferometry monitoring case study. Landslides 12(1):55–68

    Article  Google Scholar 

  • Meyer Y (1990) Ondelettes et opérateurs. Hermann, Paris

    Google Scholar 

  • Miao H, Wang G, Yin K, Kamai T, Li Y (2014) Mechanism of the slow-moving landslides in Jurassic red-strata in the Three Gorges Reservoir, China. Eng Geol 171:59–69

    Article  Google Scholar 

  • Miao F, Wu Y, Xie Y, Li Y (2018) Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides 15(3):475–488

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930

    Article  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. Environ Earth Sci 73(8):4791–4804

    Article  Google Scholar 

  • Saito M (1965) Forecasting the time of occurrence of a slope failure. In: Proceedings of the 6th international conference on soil mechanics and foundation engineering pp: 537–541

  • Saito M (1969) Forecasting time of slope failure by tertiary creep. In: Proceedings of 7th international conference on soil mechanics and foundation engineering pp: 677–683

  • Sassa K, Picarelli L, Yin Y (2009) Monitoring, prediction and early warning. In: Landslides-disaster risk reduction. Springer Berlin Heidelberg, pp 351–375

  • Sun G, Yang Y, Jiang W, Zheng H (2017) Effects of an increase in reservoir drawdown rate on bank slope stability: a case study at the Three Gorges Reservoir, China. Eng Geol 221:61–69

    Article  Google Scholar 

  • Tang H, Li C, Hu X, Wang L, Criss R, Su A, Wu Y, Xiong C (2015) Deformation response of the Huangtupo landslide to rainfall and the changing levels of the Three Gorges Reservoir. B Eng Geol Environ 74(3):933–942

    Article  Google Scholar 

  • Vasu NN, Lee S (2016) A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea. Geomorphology 263:50–70

    Article  Google Scholar 

  • Wu X, Benjamin Zhan F, Zhang K, Deng Q (2016) Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China. Environ Earth Sci 75(2):146–161

    Article  Google Scholar 

  • Yabe H, Hayasaka I (1920) Geographical research in China, 1911–1916: reports. Paleontology of Southern China. Tokyo Geographical Society, Tokyo

  • Yang Z, Ce L, Lian L (2017) Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl Energ 190:291–305

    Article  Google Scholar 

  • Zhou C, Yin K (2014) Landslide displacement prediction of WA-SVM coupling model based on chaotic sequence. Electr J Geol Eng 19:2973–2987

    Google Scholar 

  • Zhou C, Yin K, Cao Y, Ahmed B (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 

  • Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemie HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–27

    Article  Google Scholar 

  • Zhu X, Xu Q, Tang M, Nie W, Ma S, Xu Z (2017) Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: a case study in Sichuan Province, China. Eng Geol 218:213–222

    Article  Google Scholar 

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Acknowledgements

The comments from the anonymous reviewers and the editors have significantly improved the quality of this article. The first author would like to thank the China Scholarship Council for funding his research at the University of Florence, Italy.

Funding

This paper was prepared as part of the projects “The study of mechanism and forecast criterion of the gentle-dip landslides in The Three Gorges Reservoir Region, China” (no. 41572292) and “Study on the hydraulic properties and the rainfall infiltration law of the ground surface deformation fissure of colluvial landslides” (no. 41702330) funded by the National Natural Science Foundation of China.

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Authors and Affiliations

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

    Chao Zhou, Kunlong Yin & Ying Cao

  2. Department of Earth Sciences, University of Florence, 50121, Florence, Italy

    Chao Zhou, Emanuele Intrieri & Filippo Catani

  3. Institute for Risk and Disaster Reduction, University College London (UCL), WC1E 6BT, London, UK

    Bayes Ahmed

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  1. Chao Zhou
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  2. Kunlong Yin
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  3. Ying Cao
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  4. Emanuele Intrieri
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  6. Filippo Catani
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Correspondence to Kunlong Yin.

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Zhou, C., Yin, K., Cao, Y. et al. Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides 15, 2211–2225 (2018). https://doi.org/10.1007/s10346-018-1022-0

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  • DOI: https://doi.org/10.1007/s10346-018-1022-0

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