Prediction of soil-water characteristic curves using two artificial intelligence (AI) models and AI aid design method for sands

Abstract

In this paper, two artificial intelligence (AI) models (i.e., artificial neural networks (ANN) and multivariate adaptive regression splines (MARS)) were developed using cumulative percentiles from the grain-size distribution (GSD) curve as input information to predict the soil-water characteristic curve (SWCC). The importance of each input variable was testified using two different sensitivity analyses. The results show a strong correlation between the SWCC and GSD curves based on large volume of datasets. The ANN provides higher accuracy due to its unique structure; however, the MARS model facilitates in developing a regression equation that contributes to stable performance. The SWCC can be reliably predicted with the MARS regression equation using one data point from the GSD curve and bulk density information. Sensitivity analysis suggests that the prediction of the SWCC is also possible with a reasonable degree of accuracy by using single data point information from the GSD curve as an input variable. Finally, a novel AI aid design method is proposed by combining the MARS regression equation along with physico-empirical model and fitting equation that provides a rapid and reliable technique for predicting the SWCC of sands.