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

Heelak Choi, Sang-Ik Suh, Su-Hee Kim, Eun Jin Han and Seo Jin Ki
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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

Sustainability, 2021, vol. 13, issue 19, 1-11

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.

Keywords: deep learning; ARIMA; surface water quality; univariate data set; multivariate data set (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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