Prediction of Field Hydraulic Conductivity of Clay Liners Using an Artificial Neural Network and Support Vector Machine

Abstract

This paper describes the application of artificial neural network (ANN) and support vector machine (SVM) methods for prediction of field hydraulic conductivity of clay liners based on in situ test results such as compaction characteristics, lift thickness, number of lift, and soil classification tests like Atterberg’s limits and grain size. Statistical performances criteria, root mean square error, correlation coefficient, coefficient of determination, and overfitting ratio are used to compare different ANN and SVM models. Different algorithms are discussed for identification of important soil parameters affecting the hydraulic conductivity of clay liners. A model equation based on the parameters obtained using SVM is also discussed.