Unveiling the Re, Cr, and I diffusion in saturated compacted bentonite using machine-learning methods

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion mechanism. In this study, a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO4-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hbox {ReO}_{4}<^>{-}}$$\end{document}, HCrO4-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hbox {HCrO}_{4}<^>{-}}$$\end{document}, and I-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hbox {I}<^>{-}}$$\end{document} in saturated compacted bentonite under different salinities and compacted dry densities. The machine-learning models were trained using two datasets. One dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency (JAEA-DDB) and 15 publications. The other dataset, comprising 15,000 pseudo-instances, was produced using a multi-porosity model and contained eight input features. The results indicate that the former dataset yielded a higher predictive accuracy than the latter. Light gradient-boosting exhibited a higher prediction accuracy (R2=0.92\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2 = 0.92$$\end{document}) and lower error (MSE=0.01\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MSE = 0.01$$\end{document}) than the other machine-learning algorithms. In addition, Shapley Additive Explanations, Feature Importance, and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient, thereby offering valuable insights.