Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Nuraddeen Muhammad Babangida; Muhammad Raza Ul Mustafa; Khamaruzaman Wan Yusuf; Mohamed Hasnain Isa; Imran Baig

doi:10.1051/matecconf/20165904003

All issues

Volume 59 (2016)

MATEC Web of Conferences, 59 (2016) 04003

References

Open Access

Issue		MATEC Web of Conferences Volume 59, 2016 2016 International Conference on Frontiers of Sensors Technologies (ICFST 2016)


Article Number		04003
Number of page(s)		6
Section		Environmental Science and Engineering
DOI		https://doi.org/10.1051/matecconf/20165904003
Published online		24 May 2016

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[1] E. E. Alonso, N. M. Pinyol, and a. Yerro, “Mathematical Modelling of Slopes,” Procedia Earth Planet. Sci., vol. 9, pp. 64–73 (2014) [CrossRef] [Google Scholar]

[2] H. F. Yeh and C. H. Lee, “Soil water balance model for precipitation-induced shallow landslides,” Environ. Earth Sci., vol. 70, no. 6, pp. 2691–2701 (2013) [CrossRef] [Google Scholar]

[3] E. Damiano and P. Mercogliano, “Potential Effects of Climate Change on Slope Stability in Unsaturated Pyroclastic Soils,” in Landslide Science and Practice, vol. 4, C. Margottini, P. Canuti, and K. Sassa, Eds. Berlin: Springer (2013) [Google Scholar]

[4] H. Rahardjo, A. Satyanaga, and E. C. Leong, “Characteristics of Pore-Water Pressure Response in Slopes During Rainfall,” in 3dr Asian Conference on Unsaturated soils-UNSAT-ASIA, pp. 493–498. (2007) [Google Scholar]

[5] H. Rahardjo, E. C. Leong, and R. B. Rezaur, “Effect of antecedent rainfall on pore-water pressure distribution characteristics in residual soil slopes under tropical rainfall,” Hydrol. Process., vol. 22, no. 4, pp. 506–523, (2008) [CrossRef] [Google Scholar]

[6] M. R. Mustafa, R. B. Rezaur, H. Rahardjo, M. H. Isa, and A. Arif, “Artificial neural network modeling for spatial and temporal variations of pore-water pressure responses to rainfall Artificial neural network modeling for spatial and temporal variations of pore-water pressure responses to rainfall,” Adv. Meteorol., vol. 2015, pp. 1–39, (2015) [CrossRef] [Google Scholar]

[7] M. R. Mustafa, R. B. Rezaur, M. H. Isa, and H. Rahardjo, “Estimation of soil pore-water pressure variations using a thin plate spline basis function,” in WIT Transactions of the Built Environment, vol 137 pp. 615–624 (2014) [CrossRef] [Google Scholar]

[8] M. R. Mustafa, R. B. Rezaur, S. Saiedi, H. Rahardjo, and M. H. Isa, “Evaluation of MLP-ANN Training Algorithms for Modeling Soil Pore-Water Pressure Responses to Rainfall,” J. Hydrol. Eng., vol. 18, no. January, pp. 50–57, (2013) [CrossRef] [Google Scholar]

[9] M. R. Mustafa, R. B. Rezaur, M. H. Isa, S. Saiedi, and H. Rahardjo, “Effect of antecedent conditions on prediction of pore-water pressure using artificial neural networks,” Mod. Appl. Sci., vol. 6, no. 2, pp. 6–15, (2012) [CrossRef] [Google Scholar]

[10] M. R. Mustafa, R. B. Rezaur, H. Rahardjo, and M. H. Isa, “Prediction of pore-water pressure using radial basis function neural network,” Eng. Geol., vol. 135–136, pp. 40–47, (2012) [CrossRef] [Google Scholar]

[11] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121–167, Jun. (1998) [Google Scholar]

[12] R. Noori, Z. Deng, A. Kiaghadi, and F. T. Kachoosangi, “How Reliable Are ANN , ANFIS , and SVM Techniques for Predicting Longitudinal Dispersion Coefficient in Natural Rivers ?,” J. Hydraul. Eng., pp. 1–8 (2015) [Google Scholar]

[13] W. Zhao, T. Tao, and E. Zio, “Parameters tuning in support vector regression for reliability forecasting,” Chem. Eng. Trans., vol. 33, pp. 523–528 (2013) [Google Scholar]

[14] Z. A. Zakaria and A. Shabri, “Streamflow Forecasting at Ungaged Sites Using Support Vector Machines,” Appl. Math. Sci., vol. 6, no. 60, pp. 3003–3014 (2012) [Google Scholar]

[15] K. Lamorski, C. Sławiński, F. Moreno, G. Barna, W. Skierucha, and J. L. Arrue, “Modelling soil water retention using support vector machines with genetic algorithm optimisation.,” Scientific World Journal., vol. 2014 (2014) [CrossRef] [Google Scholar]

[16] B. Zhu, Z. Cheng, and H. Wang, “A kernel function Optimisation and Selection Algorithm Based on Cost Function Maximization,” in IIEEE International Conference on Imaging Systems and Techniques (IST), 2013, no. 2, pp. 259–263 (2013) [Google Scholar]

[17] V. N. Vapnik, The Nature of Statistical Learning Theory, vol. 8, no. 6. New York, New York, USA: Springer, (1995) [Google Scholar]

[18] V. Kecman, Learning and Soft Computing. London, England: MIT press, (2001) [Google Scholar]

[19] Y. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, and C. Lin, “Training and Testing Low-degree Polynomial Data Mappings via Linear SVM,” J. Mach., vol. 11, pp. 1471–1490, (2010) [Google Scholar]

[20] S. Yaman and J. Pelecanos, “Machines for Speaker Veri fi cation,” IEEE Signal Process. Lett., vol. 20, no. 9, pp. 901–904, (2013) [CrossRef] [Google Scholar]

[21] G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, (2014) [CrossRef] [Google Scholar]

[22] S. Maldonado and R. Weber, “A wrapper method for feature selection using Support Vector Machines,” Inf. Sci. (Ny)., vol. 179, no. 13, pp. 2208–2217, (2009). [CrossRef] [Google Scholar]

[23] R. Kohavi and R. Kohavi, “Wrappers for feature subset selection,” Artif. Intell., vol. 97, no. 1–2, pp. 273–324, (1997). [CrossRef] [Google Scholar]

[24] H. Mark, F. Eibe, H. Geoffrey, P. Bernhard, R. Peter, and I. H. Witten “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, vol 11, no 1 (2009) [Google Scholar]

[25] Y. EL-Manzalawy and V. Honavar, “WLSVM : Integrating LibSVM into Weka Environment,” (2005). http://www.cs.iastate.edu/~yasser/wlsv [Google Scholar]

[26] C. C. Chang, C. J. Lin “LIBSVM: A Library for Support Vector Machines,” ACM Trans Intell Syst Technol (ACM TIST) vol 2, pp. 1–27 (2011) [Google Scholar]

[27] P. A. Kagoda, J. Ndiritu, C. Ntuli, and B. Mwaka, “Application of radial basis function neural networks to short-term streamflow forecasting,” Phys. Chem. Earth, Parts A/B/C, vol. 35, no. 13–14, pp. 571–581, (2010) [CrossRef] [Google Scholar]