Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations

Interpretable Machine Learning Models and Symbolic Regressions Reveal Transfer of Per- and Polyfluoroalkyl Substances (PFASs) in Plants: A New Small-Data Machine Learning Method to Augment Data and Obtain Predictive Equations

Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. However, two challenges (small sample sizes and a lack of quantitative calculation functions) remain when using ML to predict migration in hydroponic systems. For the bioacc...

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Bibliographic Details
Main Authors: Yuan Zhang, Yanting Li, Yang Li, Lin Zhao, Yongkui Yang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Toxics
Subjects:
machine learning
data augmentation
symbolic regression
PFAS bioaccumulation
quantitative prediction
Online Access:https://www.mdpi.com/2305-6304/13/7/579
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