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Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

1
Department of Environmental Engineering, Inje University, 197 Inje-ro, Gimhae 50834, Korea
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Department of Safety and Environmental Research, The Seoul Institute, 57 Nambusunhwan-ro, 340-gil, Seoul 06756, Korea
3
Department of Civil & Environmental Engineering, Myongji University, 116 Myongji-ro, Yongin-si 17058, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: David McCarthy
Water 2021, 13(17), 2392; https://doi.org/10.3390/w13172392
Received: 7 July 2021 / Revised: 23 August 2021 / Accepted: 26 August 2021 / Published: 30 August 2021
(This article belongs to the Section Water Quality and Contamination)
We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term. View Full-Text
Keywords: probability forecast; artificial neural network (ANN); exploratory factor analysis (EFA); water quality prediction probability forecast; artificial neural network (ANN); exploratory factor analysis (EFA); water quality prediction
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MDPI and ACS Style

Jung, W.S.; Kim, S.E.; Kim, Y.D. Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting. Water 2021, 13, 2392. https://doi.org/10.3390/w13172392

AMA Style

Jung WS, Kim SE, Kim YD. Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting. Water. 2021; 13(17):2392. https://doi.org/10.3390/w13172392

Chicago/Turabian Style

Jung, Woo S., Sung E. Kim, and Young D. Kim 2021. "Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting" Water 13, no. 17: 2392. https://doi.org/10.3390/w13172392

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