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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2016

01.08.2016 | Original Article

Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

verfasst von: Jiejie Chen, Zhigang Zeng, Ping Jiang, Huiming Tang

Erschienen in: Neural Computing and Applications | Ausgabe 6/2016

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Abstract

Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.
Vorheriger Artikel Video-based face recognition and image synthesis from rotating head frames using nonlinear manifold learning by neural networks
Nächster Artikel Identifying the discriminative predictors of upper body power of cross-country skiers using support vector machines combined with feature selection

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Literatur
1.
Qin SQ, Jiao JJ, Wang SJ (2001) The predictable time scale of landslides. Bull Eng Geol Environ 59(4):307–312CrossRef
2.
Qin SQ, Jiao JJ, Wang SJ (2002) A nonlinear dynamical model of landslide evolution. Geomorphol 4:377–85
3.
Sorbino G, Sica C, Cascini L (2010) Susceptibility analysis of shallow landslides source areas using physically based models. Nat Hazards Rev 53:313–332CrossRef
4.
Francesca B, Ivan C, Paolo M, Alberto P (2014) A field experiment for calibrating landslide time-of-failure prediction functions. Int J Rock Mech Min Sci 67:69–77
5.
Chen CT, Lin ML, Wang KL (2014) Landslide seismic signal recognition and mobility for an earthquake-induced rockslide in Tsaoling, Taiwan. Eng Geol 171:31–44CrossRef
6.
Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards Rev 59(3):1491–1512CrossRef
7.
Chen HQ, Zeng ZG (2013) Deformation prediction of landslide based on improved back-propagation neural network. Cognit Comput 5(1):56–62MathSciNetCrossRef
8.
Lian C, Zeng ZG, Yao W, Tang HM (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Nat Hazards 66:759–771CrossRef
9.
Lian C, Zeng ZG, Yao W, Tang HM (2013) Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel, 2013. In: Sixth international conference on advanced computational intelligence, Hangzhou, China, pp 52–57
10.
Yao W, Zeng ZG, Lian C, Tang HM (2013) Ensembles of echo state networks for time series prediction, 2013. In: Sixth international conference on advanced computational intelligence. Hangzhou, China, pp 299–304
11.
Chen J, Zeng ZG, Jiang P, Tang HM (2015) Deformation prediction of landslide based on functional networt. Neurocomputing 149:151–157CrossRef
12.
Nefilioglu 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 97:171–191CrossRef
13.
Castillo E (1998) Functional networks. Neural Process Lett 7(3):151–159CrossRef
14.
Castillo E, Cobo A, Gutiérrez JM, Pruneda E (1998) An introduction to functional networks with applications. Kluwer Academic Publishers, New YorkMATH
15.
Bruen M, Yang JQ (2005) Functional networks in real-time flood forecasting—a novel application. Adv Water Resour 28(9):899–909CrossRef
16.
Iglesias A, Arcay B, Cotos JM, Taboada A, Dafonte C (2004) A comparison between functional networks and artificial neural networks for the prediction of fishing catches. Neural Comput Appl 13(1):24–31CrossRef
17.
Nun AI, Arcay LH, Cotos M, Varela J (2005) Optimisation of fishing prediction of artificial neural networks, anfis, functional network and remote sensing images. Expert Syst Appl 29:356–363CrossRef
18.
Castillo E, Hadi AS, Lacruz B, Pruneda RE (2008) Semi-parametric nonlinear regression and transformation using functional networks. Comput Stat Data Anal 52(4):2129–2157MathSciNetCrossRefMATH
19.
El-Sebakhy EA (2011) Functional networks as a novel data mining paradigm in forecasting software development efforts. Expert Syst Appl 8(3):2187–2194CrossRef
20.
El-Sebakhy EA, Asparouhov O, Abdulraheem AA, Al-Majed AA, Wu DH, Latinski K, Raharja I (2012) Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir. Expert Syst Appl 39(12):10359–10375CrossRef
21.
El-Sebakhy EA, Hadi AS, Faisal KA (2007) Iterative least squares functional networks classier. IEEE Trans Neural Netw 18(3):844–850CrossRef
22.
Castillo E, Luceno A (1982) A critical analysis of some variational methods in slope stability analysis. Int J Numer Anal Methods Geomech 6(2):195–209MathSciNetCrossRefMATH
23.
Castillo E, Conejo AJ, Aranda E (2008) Sensitivity analysis in calculus of variations. Some applications. SIAM Rev 50(2):294–312MathSciNetCrossRefMATH
24.
Castillo E, Luceno A, Pedregal (2008) Composition functionals in calculus of variations. Application to products and quotients. Math Models Methods Appl Sci 18(1):47–75MathSciNetCrossRefMATH
25.
Sara A, Jonas A (2003) GPLAB-A genetic programming toolbox for MATLAB. In: Proceedings of the Nordic MATLAB conference, pp 273–278
26.
Collet P, Lutton E, Raynal F (2000) Polar IFS + parisian genetic programming = efficient IFS inverse problem solving. Genet Program Evol Mach J 1(4):339–361CrossRefMATH
27.
Ochoa G, Lutton E, Burke E (2007) Cooperative royal road functions. In: Evolution artificielle, vol 10. Tours, France, pp 29–31
28.
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MathSciNetMATH
29.
Miller JF (1999) An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach, GECCO 1999. In: Proceedings of the genetic and evolutionary computation conference, Orlando, Florida, pp 1135–1142, Morgan Kaufmann, San Francisco
30.
Miller JF, Thomson P (2000) Cartesian genetic programming. In: Proceedings of the 3rd European conference on genetic programming, Edinburgh, lecture notes in computer science, vol 1802. Springer, Berlin, pp 121–132
31.
Searson DP (2009) GPTIPS: genetic programming and symbolic regression for MATLAB
32.
Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of international multi conference on engineering computer science, Hong Kong
33.
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, USAMATH
34.
Lee CH, Yang CB, Chen HH (2014) Taiwan stock investment with gene expression programming. Proc Comput Sci 35:137–146CrossRef
35.
Nazari A (2012) Prediction performance of PEM fuel cells by gene expression programming. Int J Hydrog Energy 37(24):18972–18980CrossRef
36.
Khan MM, Ahmad AM, Khan GM, Miller JF (2013) Fast learning neural networks using Cartesian genetic programming. Neurocomputing 121(9):274–289CrossRef
37.
Goldstein EB, Coco G, Murray AB (2013) Prediction of wave ripple characteristics using genetic programming. Cont Shelf Res 71:1–15CrossRef
38.
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):71–187
39.
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21:189–202CrossRef
40.
Gandomi AH, Alavi AH, Mirzahosseini MR, Moqaddas NF (2011) Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J Mater Civil Eng ASCE 23(3):248–263CrossRef
41.
Gandomi AH, Tabatabaei SM, Moradian MH, Radfar A, Alavi AH (2011) A new prediction model for the load capacity of castellated steel beams. J Construct Steel Res 67:1096–1105CrossRef
42.
Hsu CM (2011) A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Syst Appl 38:14026–14036
43.
Hsu CM (2012) Flow discharge prediction in compound channels using linear genetic programming. J Hydrol 454–455:203–207
44.
Xua Q, Chen QW, Maa JF, Blanckaerta K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. Cont Shelf Res 7:134–140
45.
Garg A, Garg A, Tai K, Barontini S, Stokes A (2014) A computational intelligence-based genetic programming approach for the simulation of soil water retention curve. Transp Porous Media 103(3):497–513CrossRef
46.
Garg A, Tai K (2013) Genetic programming for modeling vibratory finishing process: role of experimental designs and fitness functions. Swarm, evolutionary and memetic computing. Lecture notes in computer science, vol 8298, pp 23–31
47.
Garg A, Tai K (2014) Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simul Model Pract Theory 48:93–111CrossRef
48.
Vijayaraghavan V, Garg A, Wong CH, Tai K, Singrub Pravin M, Liang Gao, Sangwan KS (2014) A molecular dynamics based artificial intelligence approach for characterizing thermal transport in nanoscale material. Thermochim Acta 594(20):39–49CrossRef
49.
Garg A, Tai K (2014) An ensemble approach of machine learning in evaluation of mechanical property of the rapid prototyping fabricated prototype. Appl Mech Mater 575:493–496CrossRef
50.
Wang GJ, Xie C, Chen S, Yang JJ, Yang MY (2013) Random matrix theory analysis of cross-correlations in the US stock market: evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient. Phys A Stat Mech Appl 392:3715–3730MathSciNetCrossRef
51.
Frenzel S, Pompe B (2007) Partial mutual information for coupling analysis of multivariate time series. Phys Rev Lett 99:1–4CrossRef
52.
Kraskov A, Stogbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E Stat Nonlinear Soft Matter Phys 69:1–16MathSciNetCrossRef
53.
Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root ? J Econom 54:159–178CrossRefMATH
54.
Lian C, Zeng ZG, Yao W, Tang HM (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch Enviorn Res Risk Assess 28(8):1957–1972CrossRef
Metadaten
Titel
Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction
verfasst von
Jiejie Chen
Zhigang Zeng
Ping Jiang
Huiming Tang
Publikationsdatum
01.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1976-y

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