Abstract
Accurate landslide displacement prediction is essential for an early warning system. At present, the inputs of the data-driven models adopted in landslide displacement prediction remain unchanged. However, considering that the sensitivities of landslide displacement states to external factors are constantly changing, it is reasonable to take trigger factors’ time-varying characteristics into account for obtaining better prediction results. In this study, the input cumulative displacement was first decomposed by singular spectrum analysis, and the k-means algorithm was developed to cluster the periods representing the different states of the examined landslide. Then, a one-dimensional convolutional neural network with a detailed structure was introduced to consider time-varying inputs for periodic displacement prediction. A grey power model optimized by particle swarm optimization was proposed to predict trend displacements with less empirical judgement. Model’s performance was mainly validated based on the Baishuihe landslide in the Three Gorges Reservoir area. The application results demonstrate that (i) the periods representing different landslide states can be obtained reasonably by clustering trend information; (ii) the optimized grey power model can be utilized more universally than the polynomials for trend displacement prediction; and (iii) the consideration of time-varying trigger factors in the data-driven model further enhances the model’s prediction accuracy and robustness.
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References
Borovykh A, Bohte S, Oosterlee CW (2017) Conditional time series forecasting with convolutional neural networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp 729–730
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 13:725–736. https://doi.org/10.1007/s10346-015-0596-z
Chen H, Chen A, Xu L et al (2020) A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agric Water Manag 240:106303. https://doi.org/10.1016/j.agwat.2020.106303
Criss RE, Yao W, Li C, Tang H (2020) A predictive, two-parameter model for the movement of reservoir landslides. J Earth Sci 31:1051–1057. https://doi.org/10.1007/s12583-020-1331-9
Dang Y, Liu S, Lian Z (2009) Solution of GM(1,1) power model and its properties. Syst Eng Electron 31:2380–2383
Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides Three Gorges Reservoir China. Landslides 10:203–218. https://doi.org/10.1007/s10346-012-0326-8
Farhat NH (1992) Photonit neural networks and learning mathines the role of electron-trapping materials. IEEE Expert Syst Their Appl 7:63–72. https://doi.org/10.1109/64.163674
Ferentinou MD, Sakellariou MG (2007) Computational intelligence tools for the prediction of slope performance. Comput Geotech 34:362–384. https://doi.org/10.1016/j.compgeo.2007.06.004
Gao W, Dai S, Chen X (2020) Landslide prediction based on a combination intelligent method using the GM and ENN: two cases of landslides in the Three Gorges Reservoir China. Landslides 17:111–126. https://doi.org/10.1007/s10346-019-01273-w
Han H, Shi B, Zhang L (2021) Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change. Eng Geol 280:105876. https://doi.org/10.1016/j.enggeo.2020.105876
Haque U, Paula F, Devoli G et al (2019) Science of the total environment the human cost of global warming : deadly landslides and their triggers (1995–2014). Sci Total Environ 682:673–684. https://doi.org/10.1016/j.scitotenv.2019.03.415
He X, Xu H, Sabetamal H, Sheng D (2020) Machine learning aided stochastic reliability analysis of spatially variable slopes. Comput Geotech 126:103711. https://doi.org/10.1016/j.compgeo.2020.103711
Hsu C, Chang C, Lin C (2008) A practical guide to support vector classification. BJU Int 101:1396–1400
Huang CJ, Kuo PH (2018) A deep cnn-lstm model for particulate matter (Pm2.5) forecasting in smart cities. Sensors 18:2220. https://doi.org/10.3390/s18072220
Huang R (2007) Large-scale landslides and their sliding mechanisms in China since the 20th century. Chinese J Rock Mech Eng 26:433–454
Keqiang H, Xiangran L, Xueqing Y, Dong G (2008) The landslides in the Three Gorges Reservoir Region China and the effects of water storage and rain on their stability. Environ Geol 55:55–63. https://doi.org/10.1007/s00254-007-0964-7
Khan ZA, Hussain T, Ullah A et al (2020) Towards efficient electricity forecasting in residential and commercial buildings: a novel hybrid CNN with a LSTM-AE based framework. Sensors 20:1–16. https://doi.org/10.3390/s20051399
Kim TY, Cho SB (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81. https://doi.org/10.1016/j.energy.2019.05.230
Lahmiri S (2018) Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl Math Comput 320:444–451. https://doi.org/10.1016/j.amc.2017.09.049
Li D, Yin K, Leo C (2010) Analysis of Baishuihe landslide influenced by the effects of reservoir water and rainfall. Environ Earth Sci 60:677–687. https://doi.org/10.1007/s12665-009-0206-2
Li X, Kong J, Wang Z (2012) Landslide displacement prediction based on combining method with optimal weight. Nat Hazards 61:635–646. https://doi.org/10.1007/s11069-011-0051-y
Liao K, Wu Y, Miao F et al (2020) Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide. Bull Eng Geol Environ 79:673–685. https://doi.org/10.1007/s10064-019-01598-9
Liu Z, Tesfamariam S (2012) Prediction of lateral spread displacement: data-driven approaches. Bull Earthq Eng 10:1431–1454. https://doi.org/10.1007/s10518-012-9366-7
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proc 5th Berkeley Symp Math Stat Probab 1:281–297
Mao X, Shang P (2019) Multivariate singular spectrum analysis for traffic time series. Phys A Stat Mech Appl 526:121063. https://doi.org/10.1016/j.physa.2019.121063
Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400. https://doi.org/10.1016/j.geomorph.2006.10.035
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 15:475–488. https://doi.org/10.1007/s10346-017-0883-y
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization: an overview. Swarm Intell 1:33–57. https://doi.org/10.1007/s11721-007-0002-0
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 73:4791–4804. https://doi.org/10.1007/s12665-014-3764-x
Reshef D, Reshef Y, Mitzenmacher M, Sabeti P (2013) Equitability analysis of the maximal information coefficient with comparisons. CoRR abs/1301.6314
Saito M (1965) Forecasting the time of occurrence of a slope failure. Proc 6th Int Conf Soil Mech Found Eng 2:537–541
Sharma D, Thulasiraman K, Wu D, Jiang JN (2018) Power network equivalents: a network science based K-means clustering method integrated with silhouette analysis. In: Cherifi C, Cherifi H, Karsai M, Musolesi M (eds) Complex networks & their applications VI. Springer International Publishing, Cham, pp 78–89
Su H, Li X, Yang B, Wen Z (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427. https://doi.org/10.1016/j.ymssp.2018.03.022
Sun M, Li X, Kim G (2019) Precipitation analysis and forecasting using singular spectrum analysis with artificial neural networks. Cluster Comput 22:12633–12640. https://doi.org/10.1007/s10586-018-1713-2
Tang RX, Kulatilake PHSW, Yan EC, Sen CJ (2020) Evaluating landslide susceptibility based on cluster analysis probabilistic methods and artificial neural networks. Bull Eng Geol Environ 79:2235–2254. https://doi.org/10.1007/s10064-019-01684-y
Vidal A, Kristjanpoller W (2020) Gold volatility prediction using a CNN-LSTM approach. Expert Syst Appl 157:113481. https://doi.org/10.1016/j.eswa.2020.113481
Wang C, Zhang H, Ma P (2020) Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network. Appl Energy 259:114139. https://doi.org/10.1016/j.apenergy.2019.114139
Wang FW, Zhang YM, Huo ZT et al (2004) The July 14 2003 Qianjiangping landslide three gorges reservoir China. Landslides 1:157–162. https://doi.org/10.1007/s10346-004-0020-6
Wang Q, Song X (2019) Forecasting China’s oil consumption: a comparison of novel nonlinear-dynamic grey model (GM) linear GM nonlinear GM and metabolism GM. Energy 183:160–171. https://doi.org/10.1016/j.energy.2019.06.139
Wang W, Li J, Qu X et al (2019a) Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China. Nat Hazards 96:1121–1139. https://doi.org/10.1007/s11069-019-03595-3
Wang Y, Tang H, Wen T, Ma J (2019b) A hybrid intelligent approach for constructing landslide displacement prediction intervals. Appl Soft Comput J 81:105506. https://doi.org/10.1016/j.asoc.2019.105506
Wu S, Jin Y, Zhang Y et al (2004) Investigations and assessment of the landslide hazards of Fengdu county in the reservoir region of the Three Gorges project on the Yangtze River. Environ Geol 45:560–566. https://doi.org/10.1007/s00254-003-0911-1
Xing Y, Yue J, Chen C (2020) Interval estimation of landslide displacement prediction based on time series decomposition and long short-term memory network. IEEE Access 8:3187–3196. https://doi.org/10.1109/ACCESS.2019.2961295
Yang B, Yin K, Lacasse S, Liu Z (2019) Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides 16:677–694. https://doi.org/10.1007/s10346-018-01127-x
Zhang L, Shi B, Zhu H et al (2020) PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect. Landslides 18:179–193. https://doi.org/10.1007/s10346-020-01426-2
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. https://doi.org/10.1016/j.enggeo.2016.02.009
Zhu X, Xu Q, Tang M et al (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. https://doi.org/10.1016/j.enggeo.2017.01.022
Funding
This research has been supported by the China National Key R&D Program during the 13th Five-year Plan Period (grant nos. 2018YFC1505104 and 2017YFC1503103) and the Liao Ning Revitalization Talents Program (grant no. XLYC1807263).
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Pei, H., Meng, F. & Zhu, H. Landslide displacement prediction based on a novel hybrid model and convolutional neural network considering time-varying factors. Bull Eng Geol Environ 80, 7403–7422 (2021). https://doi.org/10.1007/s10064-021-02424-x
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DOI: https://doi.org/10.1007/s10064-021-02424-x