Prediction of landslide displacement based on GA-LSSVM with multiple factors

Bulletin of Engineering Geology and the Environment

Volumn 75, Issue 2, 2016, Pages 637-646

  Cai, Zhenglong ^a   Xu, Weiya ^a   Meng, Yongdong ^b   Shi, Chong ^a   Wang, Rubin ^a  

^a HOHAI UNIVERSITY   (China)

^b CHINA THREE GORGES UNIVERSITY   (China)

Author keywords

Landslide displacement prediction; Least squares support vector machine; Multiple factors; Wavelet decomposition

Indexed keywords

FORECASTING; GENETIC ALGORITHMS; LANDSLIDES; SUPPORT VECTOR MACHINES;

BASIC CHARACTERISTICS; CORRELATION COEFFICIENT; DISPLACEMENT PREDICTION; DISPLACEMENT-BASED; LEAST SQUARES SUPPORT VECTOR MACHINES; MULTIPLE FACTORS; PREDICTION ACCURACY; SINGLE FACTOR MODEL;

WAVELET DECOMPOSITION;

ACCURACY ASSESSMENT; DECOMPOSITION ANALYSIS; DISPLACEMENT; GENETIC ALGORITHM; LANDSLIDE; SATELLITE DATA; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

EID: 84944523039     PISSN: 14359529     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10064-015-0804-z     Document Type: Article

References (25)

1. 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
2. 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
3. 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
4. Fathi V, Montazer GA (2013) An improvement in RBF learning algorithm based on PSO for real time applications. Neurocomputing 111:169–175
5. 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
6. 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
7. 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
8. 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
9. Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231–240
10. 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
11. Li XZ, Kong JM (2014) Application of GA-SVM method with parameter optimization for landslide development prediction. Nat Hazard Earth Syst 14:525–533
12. 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
13. 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
14. 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
15. Mallat S (1989) A theory for multiresolution signal decomposition and wavelet representation. IEEE Trans Pattern Anal 11:674–693
16. Melchiorre C, Matteucci M, Azzoni A et al (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400
17. 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
18. 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
19. 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
20. 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
21. Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran 18(1):53–58
22. Sorbino G, Sica C, Cascini L (2010) Susceptibility analysis of shallow landslides source areas using physically based models. Nat Hazards 53:313–332
23. Suykens JAK, Vandewalle J (1999) Least squares support vector machines classifiers. Neural Process Lett 9(3):293–300
24. 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)
25. 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