Atterberg Limits Prediction Comparing SVM with ANFIS Model - Sherzoy, Mohammad Murtaza (Journal of Geoscience, Engineering, Environment, and Technology, 2017)
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.