Estimating the permeability coefficient of soil using CART and GMDH approaches

Abstract

Permeability coefficient of soil (k) is one of the most important parameters in groundwater studies. This study, two robust explicit data-driven methods, including Classification and Regression Trees (CART) and the Group Method of Data Handling (GMDH) were developed using characteristics of soil, i.e., clay content (CC), water content (ω), liquid limit (LL), plastic limit (PL), specific density (γ), void ratio (e) to generate predictive equations for prediction of k. When compared to CART; Mean Absolute Error (MAE) = 0.0051, Root Mean Square Error (RMSE) = 0.0088, Scatter Index (SI) = 64.00%, Correlation Coefficient (R) = 0.7841, Index agreement (Ia) = 0.8830; the GMDH equation produced the lowest error values; MAE = 0.0044, RMSE = 0.0072, SI = 52.17%, R = 0.8493, Ia = 0.9184; in testing stage. Although, GMDH had better performance, however, CART and GMDH could be considered effective approaches for the prediction of k.

Prediction of permeability coefficient of complex graded non-cohesive soil

“…In addition, many scholars found that the permeability coefficient estimation results and its range were compared with the test measurement results of various samples with different levels. They found that the two were the same (CG et al, 2020;Torabi et al, 2022).…”