Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches

Landslides

Volumn 11, Issue 5, 2014, Pages 889-896

  Liu, Zaobao ^a,b   Shao, Jianfu ^a,b   Xu, Weiya ^a   Chen, Hongjie ^a   Shi, Chong ^a  

^a HOHAI UNIVERSITY   (China)

^b UNIV LILLE   (France)

Author keywords

Computational intelligence; Displacement prediction; Gaussian process; Landslide; Nonlinear; Relevance vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); NEURAL NETWORKS; NONLINEAR ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

COLLUVIAL LANDSLIDE; COMPUTATIONAL INTELLIGENCE TECHNIQUES; DISPLACEMENT PREDICTION; GAUSSIAN PROCESSES; NONLINEAR; NONLINEAR DISPLACEMENT; OPTIMIZED PARAMETER; RELEVANCE VECTOR MACHINE;

LANDSLIDES;

EID: 84920250071     PISSN: 1612510X     EISSN: 16125118     Source Type: Journal    
DOI: 10.1007/s10346-013-0443-z     Document Type: Article

References (36)

1. Belle P, Aunay B, Bernardie S, Grandjean G, Ladouche B, Mazué R, Join J-L (2013) The application of an innovative inverse model for understanding and predicting landslide movements (Salazie cirque landslides, Reunion Island). Landslides. doi:10.1007/s10346-013-0393-5, 1–13
2. Bozzano F, Cipriani I, Mazzanti P, Prestininzi A (2011) Displacement patterns of a landslide affected by human activities: insights from ground-based InSAR monitoring. Nat Hazards 59(3):1377–1396
3. Chen M, Wang L (1988) A prediction method by grey system for slope deformation and failure. In: Proceedings of the 5th international symposium on landslides, Lausanne, pp 577–582
4. Chen H, Zeng Z (2012) Deformation prediction of landslide based on improved back-propagation neural network. Cogn Comput. doi:10.1007/s12559-012-9148-1, 1–7
5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
6. Dong H, Fu H, Feng W, Deng Z (2007) Landslide displacement prediction based on Takens theory and SVM. China J Highw Transp 20(5):13–18
7. Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides, Three Gorges Reservoir, China. Landslides 10(2):203–218. doi:10.1007/s10346-012-0326-8
8. Feng X, Zhao H, Li S (2004) Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech Min 41(7):1087–1107
9. Fukuzono T (1985) A new method for predicting the failure time of a slope. Tokyo University Press, Tokyo, pp 145–150, In: Proceedings of the 4th international conference and field workshop on landslides
10. Grelle G, Guadagno FM (2012) Regression analysis for seismic slope instability based on a double phase viscoplastic sliding model of the rigid block. Landslides. doi:10.1007/s10346-012-0350-8, 1–15
11. Jibson RW (2007) Regression models for estimating coseismic landslide displacement. Eng Geol 91(2–4):209–218
12. John C (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical report MSR-TR-98-14. Redmond
13. Li T, Chen M, Wang L, Zhou Y (1996) Time prediction of landslides using Verhulst inverse-function model. In: Proceedings of the 7th international symposium on landslides, Trondheim, pp 1289–1293
14. Li X, Kong J, Wang Z (2012) Landslide displacement prediction based on combining method with optimal weight. Nat Hazards 61(2):635–646. doi:10.1007/s11069-011-0051-y
15. Lian C, Zeng Z, Yao W, Tang H (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66(2):759–771. doi:10.1007/s11069-012-0517-6
16. Liu Z, Xu W, Zhang K, LIU K (2009a) Prediction of rock slope deformation based on optimized exponential smoothing method. J Hohai Univ (Nat Sci) 37(3):313–316
17. Liu ZB, Xu WY, Meng YD, Chen H (2009) Modification of GM (1,1) and its application in analysis of rock-slope deformation. In: Proceedings of 2009 IEEE international conference on GSIS, Nanjing, pp 415–419
18. Liu Z, Xu W, Shao J (2012) Gaussian process based approach for application on landslide displacement analysis and prediction. Comp M Eng Sci 84(2):99–122. doi:10.3970/cmes.2012.084.099
19. Liu Z, Shao J, Xu W, Meng Y (2013) Prediction of rock burst classification using the technique of cloud models with attribution weight. Nat Hazards 68(2):549–568. doi:10.1007/s11069-013-0635-9
20. Lv Y, Liu H (2012) Prediction of landslide displacement using grey and artificial neural network theories. Adv Sci Lett 11(1):511–514
21. Mayoraza F, Vullietb L (2002) Neural networks for slope movement prediction. Int J Geomech 2(2):153–173
22. Newmark NM (1965) Effects of earthquakes on dams and embankments. Geotechnique 15:139–159
23. Phillips CL, Bruno M-A, Maquet P, Boly M, Noirhomme Q, Schnakers C, Vanhaudenhuyse A, Bonjean M, Hustinx R, Moonen G, Luxen A, Laureys S (2011) “Relevance vector machine” consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients. Neuro Image 56(2):797–808
24. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT, Cambridge
25. Romeo R (2000) Seismically induced landslide displacements: a predictive model. Eng Geol 58(3=4):337–351
26. Rose ND, Hungr O (2007) Forecasting potential rock slope failure in open pit mines using the inverse-velocity method. Int J Rock Mech Min 44(2):308–320
27. Saito M (1965) Forecasting the time of occurrence of slope failure. University of Toronto Press, Montréal, pp 537–542, In: Proceedings of the 6th international conference on soil mechanics and foundation engineering
28. Saito M (1969) Forecasting time of slope failure by tertiary creep. In: Balkema AA (ed) International conference on soil mechanics and foundation engineering, Mexico City, 25–29 Aug, Rotterdam, pp 677–683
29. Sakai H (2001) Observation of ground displacement in landslide by monitoring groundwater composition, vol 42. Railway Technical Research Institute, Tokyo, Quarterly reports
30. Tipping ME (2000) The relevance vector machine. In: Solla SA, Leen TK, Müller KR (eds) Advances in neural information processing systems. MIT, Cambridge, pp 652–658
31. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1(1):211–244
32. Travelletti J, Oppikofer T, Delacourt C, Malet J, Jaboyedoff M (2008) Monitoring landslide displacement during a controlled rain experiment using a long-range terrestrial laser scanning (TLS). The International Archives of the Photogrammetry, Remote Sens Spat Inf Sci XXXVII(B5):485–490
33. Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York
34. Yang Y, Liu M (2005) The present advances and trends of landslide predictions. Soil Eng Found 19(2):61–65
35. Yi S (2007) Status and prospect of the temporal prediction of landslide activity. Chin J Eng Geophys 4(2):157–163
36. Zhu C, Hu G (2013) Time series prediction of landslide displacement using SVM model: application to Baishuihe landslide in Three Gorges reservoir area, China. Appl Mech Mater 239–240:1413–1420