Atterberg Limits Prediction Comparing SVM with ANFIS Model

  • Mohammad Murtaza Sherzoy Academy of Sciences of Afghanistan, Sher Ali Khan Watt, Shari-e-naw, Kabul, POBox 894, Afghanistan
Keywords: Atterberg limit, Support Vector Machine (SVM), Adaptive Neuro-Fuzzy inference System (ANFIS), sand, clay, silt


Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy inference Systems (ANFIS) both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS) methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R2 and root mean squared error (RMSE) value.  The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R2 = 0.987), plastic limit (R2 = 0.949) and plastic index (R2 = 0966). RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.


Download data is not yet available.


Ali, T.Y., 2011. Effect of fine particles on shear strength parameter of sand (thesis). Faculty of Civil Engineering.

Berbenni , S., Favier , V., Berveiller , M., 2017. Impact of the grain size distribution on the yield stress of heterogeneous material. Impact of the grain size distribution on the yield stress of heterogeneous material 23, 114–142.

Boser, B.E., Guyon, I.M., Vapnik, V.N. 1992 A training algorithm for optimal margin classifiers. In: In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, ACM

Chen, S.T., Yu, P.S& Tang, Y.H. 2010. Statistical downscaling of daily precipitation using support vector machine and multivariate analysis. Journal of hydrology. 385: 13-22

Cortes, C. and Vapnik, V. 1995. Support-vector networks. Machine Learning. Volume 20, Number 3. pp 273-297
Fletcher, T. 2009. Support Vector Machine Explained (online) SVM%20Explained.pdf (22Deceber2011)

IKRAM 2011. Report of Study on Debris Flow Controlling Factor and Triggering System in Peninsular Malaysia. Institute of Public Work. Malaysia

Jang, J.S.R. 1993. ANFIS: Adaptive –Network- based Fuzzy Interference System. IEEE Transaction on System, Man, and Cybernetic, 23(03):665-685

Lin, H.J. & Yeh, J.P. 2009. Optimal reduction of solution for support vector machines. Applied Mathematics and Computation. 214: 17-29

Tripathi, S., Srinivas, V.V. & Nanjundiah, R.S. 2006. Downscaling of precipitation climate change scenarios: A support vector machine approach. Journal of Hydrology. 330(3-4): 621-640

Vapnik, V.N 1995. The nature of statistical learning theory. New York: Springer Verlag.
Abstract viewed = 106 times
DOWNLOAD PDF downloaded = 68 times