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Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

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  • Seoro Lee

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Jonggun Kim

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Gwanjae Lee

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

  • Jiyeong Hong

    (Department of Earth and Environment, Boston University, Boston, MA 02215, USA)

  • Joo Hyun Bae

    (Korea Water Environment Research Institute, Chuncheon-si 24408, Korea)

  • Kyoung Jae Lim

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea)

Abstract

Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD 5 , SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

Suggested Citation

  • Seoro Lee & Jonggun Kim & Gwanjae Lee & Jiyeong Hong & Joo Hyun Bae & Kyoung Jae Lim, 2021. "Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10435-:d:638781
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    References listed on IDEAS

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    1. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    2. Joo Hyun Bae & Jeongho Han & Dongjun Lee & Jae E Yang & Jonggun Kim & Kyoung Jae Lim & Jason C Neff & Won Seok Jang, 2019. "Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    3. So Young Woo & Chung Gil Jung & Ji Wan Lee & Seong Joon Kim, 2019. "Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    4. Vahid Nourani & Huseyin Gokcekus & Gebre Gelete & Haitham Afan, 2021. "Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model," Complexity, Hindawi, vol. 2021, pages 1-19, February.
    5. Alper Taner & Yeşim Benal Öztekin & Hüseyin Duran, 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
    6. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    7. Yan Liu & Ting Zhang & Aiqing Kang & Jianzhu Li & Xiaohui Lei, 2021. "Research on Runoff Simulations Using Deep-Learning Methods," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
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    Cited by:

    1. Hyunkyu Shin & Yonghan Ahn & Sungho Tae & Heungbae Gil & Mihwa Song & Sanghyo Lee, 2021. "Enhancement of Multi-Class Structural Defect Recognition Using Generative Adversarial Network," Sustainability, MDPI, vol. 13(22), pages 1-13, November.
    2. Jian Sun & Baizhong Yan & Yao Li & Huixiao Sun & Yahui Wang & Jiaqi Chen, 2021. "Characterization and Cause Analysis of Shallow Groundwater Hydrochemistry in the Plains of Henan Province, China," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
    3. Babak Mohammadi, 2022. "Application of Machine Learning and Remote Sensing in Hydrology," Sustainability, MDPI, vol. 14(13), pages 1-2, June.

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