| Enhanced classification of dissolved organic matter sources based on rivers using machine learning and data augmentation |
| Jinho Kim1, Junho Jeon2, Jongkwan Park2†, and Donghyeok An1† |
1Department of Computer Engineering, Changwon National University, Changwon 51140, South Korea
2Department of Environment & Energy Engineering, Changwon National University, Changwon, 51140, South Korea |
Corresponding Author:
Jongkwan Park ,Tel: +82-55-213-3742, Fax: +82-55-213-3749, Email: jkpark2019@changwon.ac.kr
Donghyeok An ,Tel: +82-55-213-3742, Fax: +82-55-213-3749, Email: jkpark2019@changwon.ac.kr
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Received: December 27, 2024; Accepted: February 10, 2025. |
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| ABSTRACT |
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This paper investigates the improvement in organic matter classification accuracy from different aquatic environments through the application of machine learning and deep learning techniques, supplemented with data generated by an LSTM-GAN model. Samples from the Nakdong and Yeongsan Rivers in South Korea were analyzed using Orbitrap HR-MS to obtain natural organic matter (NOM) data. Classification was performed using three machine learning algorithms—random forest, support vector machine (SVM), and logistic regression—and one deep learning algorithm, a multi-layer perceptron (MLP). Due to the limited performance of deep learning with insufficient data, an LSTM-GAN-based augmentation model was proposed, improving MLP performance. The MLP with augmented data achieved the highest classification accuracy (79% for Yeongsan River, 68% for Nakdong River), demonstrating the significant potential of LSTM-GAN in enhancing deep learning models for river classification tasks. This approach provides a robust framework for improving environmental monitoring through machine learning. |
| Keywords:
Data augmentation | Deep Learning | DOM | LSTM-GAN | Orbitrap MS |
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