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Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

Author

Listed:
  • Heelak Choi

    (Water Quality Assessment Research Division, Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea)

  • Sang-Ik Suh

    (Department of Environmental Engineering, Gyeongsang National University, Jinju 52725, Korea)

  • Su-Hee Kim

    (Department of Environmental Engineering, Gyeongsang National University, Jinju 52725, Korea)

  • Eun Jin Han

    (Water Quality Assessment Research Division, Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea)

  • Seo Jin Ki

    (Department of Environmental Engineering, Gyeongsang National University, Jinju 52725, Korea)

Abstract

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31–18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32–404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

Suggested Citation

  • Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10690-:d:643640
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    References listed on IDEAS

    as
    1. Farid Saberi-Movahed & Mohammad Najafzadeh & Adel Mehrpooya, 2020. "Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 529-561, January.
    2. Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
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