Assessment of infiltration models developed using soft computing techniques

Abstract

In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip’s model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate.

Keywords

• infiltration rate
• support vector machines
• gaussian process based regression
• artificial neural network
• random forest regression