Machine learning the apparent diffusion coefficient of Se(IV) in compacted bentonite

分类： 核科学技术 >> 辐射物理与技术 提交时间： 2024-05-22

Xiaoqiong Shi Junlei Tian Jiacong Shen Zhengye Feng Jiaxing Feng Tao Wu Qingfeng Li

摘要： Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) algorithms were used to predict the apparent diffusion coefficient of Se(IV) in compacted bentonite.Seven instances of Se(IV)were measured using through-diffusion method.LightGBM (R2= 0.98 and RMSE = 0.025) exhibited superior predictive accuracy with a trainingdataset consisting of 956instances and eight input featuresfrom Japan Atomic Energy Agency(JAEA-DDB).Shapley Additive Explanation and Partial Dependence Plots analysesrevealedvaluable insightsinto the diffusion mechanism of adsorbed anion obtained by evaluating the relationshipsbetween the apparent diffusion coefficient and the dependency of each input feature.

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