Data-driven prediction and analysis method for nanoparticle transport behavior in porous media
作者： Kaibo Zhou , Shangyuan Li , Xiang Zhou , Yangxiang Hu , Changhe Zhang
DOI: 10.1016/J.MEASUREMENT.2020.108869
关键词:
摘要: Abstract Engineering nanoparticles, as one of the application tools nanotechnology, their transport behavior is closely related to applications such reservoir sensing and environmental protection. Therefore, it necessary develop a general method predict analyze nanoparticle behavior. In this paper, data-driven prediction analysis for in porous media proposed. Firstly, dataset containing 411 samples established, which missing data are effectively filled by random forest combining one-hot encoding. Then, categorical boosting algorithm combined with synthetic minority oversampling technique used retention fraction profile. Finally, Shapley additive explanation (SHAP) adopted feature significance. The results show that proposed has good performance on described At same time, interpretability SHAP analyzing nanoparticles also verified, provides new perspective further research application.
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