2026-10-11T19:57:01Z https://www.scidb.cn/oai

10.57760/sciencedb.j00186.00710 2025-05-06T15:35:56Z

2025-05-06 Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning doi:10.57760/sciencedb.j00186.00710 en Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of machine learning models. A regression-based missing data imputation method using light gradient boosting machine algorithm was employed to impute over 60% of the missing data. machine learning; radionuclide diffusion; bentonite; missing data Jun-Lei Tian Jia-Xing Feng Jia-Cong Shen Lei Yao Jing-Yan Wang Tao Wu Yao-Lin Zhao RESTRICTED dataset http://www.doi.org/10.1007/s41365-025-01759-4 Science Data Bank