Open Access
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
Published online 24 May 2016
  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]
  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]
  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)
  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)
  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]
  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]
  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]
  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]
  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]
  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]
  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) [NASA ADS] [CrossRef]
  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)
  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)
  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)
  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]
  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)
  17. V. N. Vapnik, The Nature of Statistical Learning Theory, vol. 8, no. 6. New York, New York, USA: Springer, (1995) [CrossRef]
  18. V. Kecman, Learning and Soft Computing. London, England: MIT press, (2001)
  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)
  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]
  21. G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Comput. Electr. Eng., vol. 40, no. 1, pp. 16–28, (2014) [CrossRef]
  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]
  23. R. Kohavi and R. Kohavi, “Wrappers for feature subset selection,” Artif. Intell., vol. 97, no. 1–2, pp. 273–324, (1997). [CrossRef]
  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)
  25. Y. EL-Manzalawy and V. Honavar, “WLSVM : Integrating LibSVM into Weka Environment,” (2005).
  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) [NASA ADS] [CrossRef] [MathSciNet]
  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]

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