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Accurate prediction of the uptake and translocation of emerging contaminants in plants has serious implications for assessing impacts on ecosystems and human health. However, traditional modeling approaches are not reliable in the prediction of transpiration stream concentration factor (TSCF) and root concentration factor (RCF). This study applied deep neural networks (DNN), recurrent neural networks (RNN), and long short-term memory (LSTM) to enhance the accuracy of predictive models for TSCF and RCF. The predictions and feature importance analysis were based on nine chemical properties and two plant root macromolecular compositions. The results indicated that deep learning models predict TSCF and RCF with improved accuracy compared to mechanistic models. The coefficient of determination (R^2) for the DNN, RNN, and LSTM models in predicting TSCF was 0.62, 0.67, and 0.56, respectively. The corresponding mean squared error (MSE) on the test set for the models was 0.055, 0.035, and 0.06, respectively. The R^2 for the DNN, RNN, and LSTM models in predicting RCF was 0.90, 0.91, and 0.84, respectively. The corresponding MSE for the models was 0.124, 0.071, and 0.126, respectively. The results of feature extraction using extreme gradient boosting underlined the importance of lipophilicity and root lipid fraction.
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This content will become publicly available on May 15, 2026
Designing Hybrid Deep Learning Models for Predicting the Fate and Transport of Organic Contaminants
Predicting the transpiration stream concentration factor (TSCF) and other concentration factors is essential in understanding the plant uptake of organic contaminants. Traditional mechanistic and numerical modeling methods often fail to reliably predict the TSCF. This study developed a hybrid deep model to predict TSCF by integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN-LSTM model used eight physicochemical properties of organic contaminants to predict TSCF. The training procedure for this hybrid model was successful. The results indicated the training and test losses for predicting TSCF were both in the same order and close to zero. This study showed that the hybrid CNN-LSTM model can outperform mechanistic models and have higher performances compared to classical machine learning models. Feature importance analysis using extreme gradient boosting highlighted the role and importance of lipophilicity in predicting uptake and translocation of organic contaminants.
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- PAR ID:
- 10610879
- Publisher / Repository:
- American Society of Civil Engineers
- Date Published:
- ISBN:
- 9780784486184
- Page Range / eLocation ID:
- 272 to 282
- Format(s):
- Medium: X
- Location:
- Anchorage, Alaska
- Sponsoring Org:
- National Science Foundation
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