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Article

Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

1
Department of Water Environment Research, National Institute of Environmental Research, Incheon 22689, Korea
2
Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Korea
3
Intelligent Network Research Section, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
4
Department of Statistics, Sungshin Women’s University, Seoul 02844, Korea
5
Department of Civil and Environmental Engineering, Hanbat National University, Daejeon 34158, Korea
*
Authors to whom correspondence should be addressed.
Both authors contributed equally to this manuscript.
Water 2020, 12(6), 1822; https://doi.org/10.3390/w12061822
Received: 18 May 2020 / Revised: 17 June 2020 / Accepted: 23 June 2020 / Published: 25 June 2020
Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), and Long–Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1–step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold–out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1–step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance. View Full-Text
Keywords: machine learning; recurrent neural network; long–short-term memory; 1–step ahead recursive prediction; variable selection; water quality; chlorophyll-a machine learning; recurrent neural network; long–short-term memory; 1–step ahead recursive prediction; variable selection; water quality; chlorophyll-a
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MDPI and ACS Style

Shin, Y.; Kim, T.; Hong, S.; Lee, S.; Lee, E.; Hong, S.; Lee, C.; Kim, T.; Park, M.S.; Park, J.; Heo, T.-Y. Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods. Water 2020, 12, 1822. https://doi.org/10.3390/w12061822

AMA Style

Shin Y, Kim T, Hong S, Lee S, Lee E, Hong S, Lee C, Kim T, Park MS, Park J, Heo T-Y. Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods. Water. 2020; 12(6):1822. https://doi.org/10.3390/w12061822

Chicago/Turabian Style

Shin, Yuna, Taekgeun Kim, Seoksu Hong, Seulbi Lee, EunJi Lee, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Jungsu Park, and Tae-Young Heo. 2020. "Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods" Water 12, no. 6: 1822. https://doi.org/10.3390/w12061822

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