Abstract
This paper presents a new model for predicting the displacement of a landslide based on the least-squares support vector machine (LSSVM) with multiple factors and a genetic algorithm (GA) is used to optimize the parameters of the LSSVM model. First, based on original monitoring displacement data, single factor GA-LSSVM models are established with and without wavelet decomposition. Second, from the analysis of the basic characteristics of a landslide, the main influencing factors of landslide displacement are identified according to their correlation coefficients. A multifactor GA-LSSVM model is then established for the prediction of landslide displacement. A case study of a landslide reveals that wavelet decomposition can efficiently improve the prediction accuracy of the GA-LSSVM model. In addition, the multifactor GA-LSSVM model performs consistently better than the single factor models for the same measurements.
References
Abbasi M, Abduli MA, Omidvar B et al (2014) Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environ Prog Sustain 33(1):220–228
An X, Jiang D, Liu C et al (2011) Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Syst Appl 38:11280–11285
De Giorgi MG, Campilongo S, Ficarella A (2014) Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7:5251–5272
Fathi V, Montazer GA (2013) An improvement in RBF learning algorithm based on PSO for real time applications. Neurocomputing 111:169–175
Garg A, Tai K (2011) A hybrid genetic programming artificial neural network approach for modeling of vibratory finishing process. Int Conf Inf Intell Comput 18:14–19
Garg A, Tai K (2014) Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process. Adv Eng Softw 78:16–27
Garg A, Vijayaraghavan V, Wong CH et al (2014) Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simul Model Pract Theory 48:93–111
Hernandez L, Baladron C, Aguiar JM et al (2013) Experimental analysis of the input variables’ relevance to forecast next day’s aggregated electric demand using neural networks. Energies 6:2927–2948
Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240
Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an artificial neural network (ANN). Geomorphology 113:97–109
Li XZ, Kong JM (2014) Application of GA-SVM method with parameter optimization for landslide development prediction. Nat Hazard Earth Syst 14:525–533
Li CD, Tang HM, Hu XL et al (2009) Landslide prediction based on wavelet analysis and cusp catastrophe. J Earth Sci China 20(6):971–977
Li DY, Wang Y, Chen LX et al (2013) Displacement prediction of Bazimen landslide with step-like deformation in the three gorges reservoir. Disaster Adv 6:185–191
Lian C, Zeng ZG, Yao W (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66:759–771
Mallat S (1989) A theory for multiresolution signal decomposition and wavelet representation. IEEE Trans Pattern Anal 11:674–693
Melchiorre C, Matteucci M, Azzoni A et al (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400
Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4(1):1–15
Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban 34(3):216–235
Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18(3):625–644
Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran 18(1):53–58
Sorbino G, Sica C, Cascini L (2010) Susceptibility analysis of shallow landslides source areas using physically based models. Nat Hazards 53:313–332
Suykens JAK, Vandewalle J (1999) Least squares support vector machines classifiers. Neural Process Lett 9(3):293–300
Zhang XD, Feng SY, Wang CJ (2011) Support vector machine model for predicting sand liquefaction based on Grid-Search method. Chin J Appl Mech 28(1):24–28 (in Chinese)
Zhang Z, Yang J, Wang Y et al (2014) Ash content prediction of coarse coal by image analysis and GA-SVM. Powder Technol 268:429–435
Acknowledgments
The work presented in this paper is financially supported by the National Key Technology R&D Program (No.2013BAB06B01) and the National Natural Science Foundation of China (No. 51309089).
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Cai, Z., Xu, W., Meng, Y. et al. Prediction of landslide displacement based on GA-LSSVM with multiple factors. Bull Eng Geol Environ 75, 637–646 (2016). https://doi.org/10.1007/s10064-015-0804-z
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DOI: https://doi.org/10.1007/s10064-015-0804-z