Abstract
There are two technical challenges in predicting slope deformation. The first one is the random displacement, which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide. The second one is the dynamic evolution of a landslide, which could not be feasibly simulated simply by traditional prediction models. In this paper, a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria (SSSC-EMD) and deep bidirectional long short-term memory (DBi-LSTM) neural network. In the proposed model, the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components, viz. trend displacement, periodic displacement, and random displacement. Then, by analyzing the evolution pattern of a landslide and its key factors triggering landslides, appropriate influencing factors are selected for each displacement component, and DBi-LSTM neural network to carry out multi-data-driven dynamic prediction for each displacement component. An accumulated displacement prediction has been obtained by a summation of each component. For accuracy verification and engineering practicability of the model, field observations from two known landslides in China, the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation. The case study verified that the model proposed in this paper can better characterize the “stepwise” deformation characteristics of a slope. As compared with long short-term memory (LSTM) neural network, support vector machine (SVM), and autoregressive integrated moving average (ARIMA) model, DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation, with the mean absolute percentage error reduced by 3.063%, 14.913%, and 13.960% respectively, and the root mean square error reduced by 1.951 mm, 8.954 mm and 7.790 mm respectively. Conclusively, this model not only has high prediction accuracy but also is more stable, which can provide new insight for practical landslide prevention and control engineering.
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References
Adnan MSG, Rahman MS, Ahmed N, et al. (2020) Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sens-Basel 12(20): 3347. https://doi.org/10.3390/rs12203347
Behnke R, Kaliske M (2018) Square block foundation resting on an unbounded soil layer: Long-term prediction of vertical displacement using a time homogenization technique for dynamic loading. Soil Dyn Earthq Eng 115: 448–471. https://doi.org/10.1016/j.soildyn.2018.07.045
Bei C, Hu B (2017) Influence of Prior Knowledge on Neural Networks Model in NLP Tasks. Chin J Inform Sci 31(6): 10–17.(In Chinese)
Chen QH, Huang N, Riemenschneider S, et al. (2006) A B-spline approach for empirical mode decompositions. Adv Comput Math 24(1): 171–195. https://doi.org/10.1007/s10444-004-7614-3
Deng DM, Liang Y, Wang QL, et al. (2017) PSO-SVR prediction method for landslide displacement based on reconstruction of time series by EEMD: A case study of landslides in Three Gorges Reservoir area. Rock Soil Mech 38(4): 1001–1009. (In Chinese) https://doi.org/10.16089/j.cnki.1008-2786.000582
Dikshit A, Pradhan B, Alamri AM (2020) Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Res 100:290–301. https://doi.org/10.1016/j.gr.2020.08.007
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(1): 111–126.
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5–6): 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
Gu DM, Huang D, Yang WD, et al. (2017) Understanding the triggering mechanism and possible kinematic evolution of a reactivated landslide in the Three Gorges Reservoir. Landslides 10: 1–5. https://doi.org/10.1007/s10346-017-0845-4
Guo ZZ, Chen LX, Gui L, et al. (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
He KQ, Guo L, Chen WG (2015) Research on displacement dynamic evaluation and prediction model of colluvial landslides induced by rainfall. Chin J Rock Mech Eng 34(2): 4204–4215. (In Chinese) https://doi.org/10.13722/j.cnki.jrme.2014.1010
He KQ, Wang ZL, Ma XY, et al. (2015) Research on the displacement response ratio of groundwater dynamic augment and its application in evaluation of the slope stability. Environ Earth Sci 74(7): 5773–5791. https://doi.org/10.1007/s12665-015-4595-0
He Q, Shahabi H, Shirzadi A, et al. (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663: 1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hu JM, Heng JN, Wen JM, et al. (2020) Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm. Renew Energ 162: 1208–1226. https://doi.org/10.1016/j.renene.2020.08.077
Hu YB, Wang LY (2005) Research on effects of permeability pressure on slope stability during regulating water level in Three Gorges Reservoir. Chin J Rock Mech Eng 24(16): 2994–2997. (In Chinese) https://doi.org/10.1007/s11629-019-5470-3
Huang FM, Yin KL, He T, et al. (2016) Influencing factor analysis and displacement prediction in reservoir landslide-a case study of three gorges reservoir, China. Teh Vjesn 23(2): 617–626. https://doi.org/10.17559/TV-20150314105216
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. P Roy Soc A-Math Phy 454: 903–995. https://doi.org/10.1098/rspa.1998.0193
Lee K, Suk J, Kim H, et al. (2021) Modeling of rainfall-induced landslides using a full-scale flume test. Landslides 18(3): 1153–1162. https://doi.org/10.1007/s10346-020-01563-8
Li DY, Sun YQ, Yin KL, et al. (2019) Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir. J Mt Sci 16(9): 2203–2214. https://doi.org/10.1007/s11629-019-5470-3
Li LM, Cheng CK, Wen ZZ (2021) Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model. J Mt Sci 18(8): 2130–2142. https://doi.org/10.1007/s11629-020-6396-5
Li LM, Zhang MY, Wen ZZ (2021) Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network. J Mt Sci 18(10): 2597–2611. https://doi.org/10.1007/s11629-021-6824-1
Li LW, Wu YP, Miao SF, et al. (2018) Displacement prediction of landslides 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
Li SH, Wu LZ, Chen JJ, et al. (2020) Multiple data-driven approach for predicting landslide deformation. Landslides 17(3): 709–718. https://doi.org/10.1007/s10346-019-01320-6
Li YY, Sun RL, Yin KL, et al. (2019) Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model. Comput Geosci 9: 111–126. https://doi.org/10.1038/s41598-019-56405-y
Lian C, Zeng ZG, Wang XP, et al. (2020) Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization. Natural Networks 130: 286–296. https://doi.org/10.1016/j.neunet.2020.07.020
Lian C, Zeng ZG, Yao W, et al. (2016) Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights. IEEE T Neur Net Lear 27(12): 2683–2695. https://doi.org/10.1109/TNNLS.2015.2512283
Lian C, Zhu LZ, Zeng ZG, et al. (2018) Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched. Neurocomputing 291: 1–10. https://doi.org/10.1016/j.neucom.2018.02.046
Liu CL, Wu YJ, Zhen CG (2015) Rolling Bearing Fault Diagnosis Based on Variational Mode Decomposition and Fuzzy C Means Clustering. Proc CSEE 35(13): 3358–3365. https://doi.org/10.13334/j.0258-8013.pcsee.2015.13.020
Liu ZL (2021). Robust Empirical Mode Decomposition (REMD). https://ww2.mathworks.cn/matlabcentral/fileexchange/70032-emd-with-soft-sifting-stopping-criterion (Accessed on 6 July 2021)
Ma R, Zeng WY, Song GC, et al. (2021) Pythagorean fuzzy C-means algorithm for image segmentation. Int J Intell Syst 36(3): 1223–1243. https://doi.org/10.1002/int.22339
Mao FS, Wu YP, Xie YH, et al. (2018) Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides 15(3): 475–488. https://doi.org/10.1007/s10346-017-0883-y
Mao J, Liu XN, Zhang C, et al. (2021) Runout prediction and deposit characteristics investigation by the distance potential-based discrete element method: the 2018 Baige landslides, Jinsha River, China. Landslides 18(1): 235–249. https://doi.org/10.1007/s10346-020-01501-8
Miao SJ, Hao X, Guo XL, et al (2017) Displacement and landslide forecast based on an improved version of Saito’s method together with the Verhulst-Grey model Arabian J Geosci 10(3): 53. https://doi.org/10.1007/s12517-017-2838-y
Murugan RS, Vinodh S (2021) Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component. Rapid Prototyping J 27(1): 155–175. https://doi.org/10.1108/RPJ-10-2019-0269
Myronidis D, Charalambos P, Stavros T (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81(1): 245–263. https://doi.org/10.1007/s11069-015-2075-1
Park SJ, Lee CW, Lee S, et al. (2018) Landslide susceptibility mapping and comparison using decision tree models: A Case Study of Jumunjin Area, Korea. Remote Sens-Basel 10(10): 1545. https://doi.org/10.3390/rs10101545
Pasculli A, Calista M, Sciarra N (2018) Variability of local stress states resulting from the application of Monte Carlo and finite difference methods to the stability study of a selected slope. Eng Geol 245: 370–389. https://doi.org/10.1016/j.enggeo.2018.09.009
Peng DD, Liu ZL, Jin YQ, et al. (2019) Improved EMD with a Soft Sifting stopping Criterion and Its Application to Fault Diagnosis of Rotating Machinery. J Mech Eng 55(10): 122–132. (In Chinese) https://doi.org/10.3901/JME.2019.10.122
Saha R, Ginwal HS, Chandra G, et al. (2020) Integrated assessment of adventitious rhizogenesis in Eucalyptus: root quality index and rooting dynamics. J Forestry Res 31(6): 2145–2161. https://doi.org/10.1007/s11676-019-01040-6
Shen SL, Njock PGA, Zhou AN, et al. (2021) Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning. Acta Geotech 16(1): 303–315. https://doi.org/10.1007/s11440-020-01005-8
Shihabudheen KV, Pillal GN, Peethambaran B (2017) Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (Elanfis). Appl Soft Comput 61(1): 892–904. https://doi.org/10.1016/j.asoc.2017.09.001
Tan LY, Huang RQ, Pei XJ (2021) Deformation characteristics and inducing mechanisms of a super-large bedding rock landslide triggered by reservoir water level decline in Three Gorges Reservoir area. Chin J Rock Mech Eng 40(2): 302–314. (In Chinese). https://doi.org/10.13722/j.cnki.jrme.2020.0728
Wang CS, Liu NY, Wang SL, et al. (2018) Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite lithium-ion battery. Appl Sci 8(11): 2078. https://doi.org/10.3390/app8112078
Wei JB, Zhao Z, Xu C, et al. (2019) Numerical investigation of landslide kinetics for the recent Mabian landslide (Sichuan, China). Landslides 16(11): 2287–2298. https://doi.org/10.1007/s10346-019-01237-0
Wu PH, Bedoya M, White J, et al. (2021) Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography Med Phys 48(2): 703–714. https://doi.org/10.1002/mp.14623
Xie PH, Zhou AG, Chal B (2019) The Application of Long Short-Term Memory(LSTM) Method on Displacement Prediction of Multifactor-Induced Landslides. IEEE Access 7: 54305–54311. https://doi.org/10.1109/ACCESS.2019.2912419
Xing Y, Yue JP, Chen C, et al. (2019) Dynamic displacement forecasting of Dashuitian landslide in China using variational mode decomposition and stack long short-term memory network. Appl Sci-Basel 9(15): 2951. https://doi.org/10.3390/app9152951
Xing Y, Yue JP, 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
Xiong J, Sun M, Zhang H, et al. (2019) Application of the Levenburg-Marquardt back propagation neural network approach for landslide risk assessments. Nat Hazard Earth Sys 19(3): 629–653. https://doi.org/10.5194/nhess-19-629-2019
Xiong K, Wu Y, Wang L, et al. (2019) Analysis of deformation and failure mechanism of Bazimen Landslide in Three Gorges Reservoir Area. Chin J Geo Hazard Control 30(5): 9–18. (In Chinese) https://doi.org/10.16031/j.cnki.issn.1003-8035.2019.05.02
Xu JD, Zhao TY, Feng GZ, et al. (2021) A Fuzzy C-Means Clustering Algorithm Based on Spatial Context Model for Image Segmentation. Int J Fuzzy Syst 23(3): 816–832. https://doi.org/10.1007/s40815-020-01015-4
Xue L, Qin SQ, Li P, et al. (2014) New quantitative displacement criteria for slope deformation process: From the onset of the accelerating creep to brittle rupture and final failure. Eng Geol 182(19): 79–87. https://doi.org/10.1016/j.enggeo.2014.08.007
Yang BB, Yin KL, Du J (2018) A model for predicting 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). https://doi.org/10.13722/j.cnki.jrme.2018.0468
Yang BB, Yin KL, Lacasse S, et al. (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
Ying CY, Zhang K, Wang ZN, et al. (2021) Analysis of the runout processes of the Xinlu Village landslide using the generalized interpolation material point method. Landslides 18(4): 1519–1529. https://doi.org/10.1007/s10346-020-01581-6
Yun B, Ma EL, Cai JJ, et al. (2021) A prediction model for rock planar slides with large displacement triggered by heavy rainfall in the Red bed area, Southwest, China. Landslides 18(2): 773–783. https://doi.org/10.1007/s10346-020-01528-x
Zeng A, Nie WJ (2019) Stock Recommendation System Based on Deep Bidirectional LSTM. Comput Sci 46(10): 84–89. (In Chinese)
Zhai CL, Chen XW (2020) Damage assessment of the target area of the island/reef under the attack of missile warhead. Def Technol 16(1): 18–28. https://doi.org/10.1016/j.dt.2019.06.022
Zhao JP, Huang DJ (2001) Mirror Extending and Circular Spline Function for Empirical Mode Decomposition Method. J Zhejiang Univ 3: 247–252. https://doi.org/10.1631/jzus.2001.0247
Zhang K, Zhang K, Bao R, et al. (2021) Intelligent prediction of landslide displacements based on optimized empirical mode decomposition and K-Mean clustering. Rock Soil Mech 42(1): 211–223. (In Chinese) https://doi.org/10.16285/j.rsm.2020.1300
Zhang L, Huang YW, Xuan J, et al. (2021) Trust evaluation model based on PSO and LSTM for huge information environments. Chinese J Electron 30(1): 92–101. https://doi.org/10.1049/cje.2020.12.005
Zhang L, Shi B, Zhu HH, et al. (2021) PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect. Landslides 18(1): 179–193. https://doi.org/10.1007/s10346-020-01426-2
Zhang Y, Wang X, Tang H (2019) An improved Elman neural network with piecewise weighted gradient for time series prediction. Neurocomputing 359: 199–208. https://doi.org/10.1016/j.neucom.2019.06.001
Zhou C, Yin KL, Cao Y, et al. (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
Zhu X, Xu Q, Tang MG, et al. (2018) A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides. Neural Comput Appl 30(12): 3825–3835. https://doi.org/10.1007/s00521-017-2968-x
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Zhang, My., Han, Y., Yang, P. et al. Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network. J. Mt. Sci. 20, 637–656 (2023). https://doi.org/10.1007/s11629-022-7638-5
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DOI: https://doi.org/10.1007/s11629-022-7638-5