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This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNNLSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration.
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This content will become publicly available on December 1, 2025
Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions
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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- PAR ID:
- 10575306
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Journal of Hazardous Materials
- Volume:
- 480
- Issue:
- C
- ISSN:
- 0304-3894
- Page Range / eLocation ID:
- 135921
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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