Prediction of attachment efficiency using machine learning on a comprehensive database and its validation

Allan Gomez-Flores,Scott A Bradford,Li Cai,Martin Urík,Hyunjung Kim

Water Research（2023）

引用 3|浏览17

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摘要

•A database comprising 22 variables and 2538 records was built.•The database was used to model attachment efficiency.•Modeling was conducted using random forest handling missing data.•Validation was conducted using unseen data and deposition experiments data.

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关键词

Attachment efficiency,Machine learning,Missing data,Colloid deposition

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