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<p>Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning (ML) models. In this study, regression-based missing data imputation method using a light gradient boosting machine (LGBM) algorithm was employed to impute more than 60% of the missing data, establishing a radionuclide diffusion dataset containing 16 input features and 813 instances. The effective diffusion coefficient (<i>D</i><sub>e</sub>) was predicted using ten ML models. The predictive accuracy of the ensemble meta-models, namely LGBM-extreme gradient boosting (XGB) and LGBM-categorical boosting (CatB), surpassed that of the other ML models, with <i>R</i><sup>2</sup> values of 0.94. The models were applied to predict the <i>D</i><sub>e</sub> values of EuEDTA<sup>−</sup> and HCrO<sub>4</sub><sup>−</sup> in saturated compacted bentonites at compactions ranging from 1200 to 1800&#xa0;kg/m<sup>3</sup>, which were measured using a through-diffusion method. The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the <i>D</i><sub>e</sub> of HCrO<sub>4</sub><sup>−</sup>. Shapley additive explanations identified total porosity as the most significant influencing factor. Additionally, the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis. This study provides a regression imputation technique to refine radionuclide diffusion datasets, offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.</p>

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Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Jun-Lei Tian,
  • Jia-Xing Feng,
  • Jia-Cong Shen,
  • Lei Yao,
  • Jing-Yan Wang,
  • Tao Wu,
  • Yao-Lin Zhao

摘要

Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning (ML) models. In this study, regression-based missing data imputation method using a light gradient boosting machine (LGBM) algorithm was employed to impute more than 60% of the missing data, establishing a radionuclide diffusion dataset containing 16 input features and 813 instances. The effective diffusion coefficient (De) was predicted using ten ML models. The predictive accuracy of the ensemble meta-models, namely LGBM-extreme gradient boosting (XGB) and LGBM-categorical boosting (CatB), surpassed that of the other ML models, with R2 values of 0.94. The models were applied to predict the De values of EuEDTA− and HCrO4− in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m3, which were measured using a through-diffusion method. The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the De of HCrO4−. Shapley additive explanations identified total porosity as the most significant influencing factor. Additionally, the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis. This study provides a regression imputation technique to refine radionuclide diffusion datasets, offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.