20200325_Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea
1. Title, Journal, and Authors
Title: Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea
Journal: Science of the Total Environment
Authors: Youngeun Parka, Kyung Hwa Chob, Jihwan Parka, Sung Min Chac, Joon Ha Kima*
a. School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea
b. School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea
c. Jeollanam-do Environmental Industries Promotion Institute, 650-94 Songgye-ro, Songjeon-myeon, Gangjin-gun, Jeollanam-do, 527-811, Republic of Korea
To establish the reliable early-warning predictions of algal bloom, researching the optimal prediction model between ANN and SVM is carried. Finding the sampling area featured data and selecting the effective prediction interval are also done. Chl-a is predicted for the substitution of direct algal concentrations.
When analyzing the prediction accuracies, they are almost the same, but SVM shows better performance than ANN. However, the significant point is SVM explains the sampling area featured data better than ANN. 7 days prediction shows the highest prediction accuracy among three interval predictions.
Dae Seong Jeong / Integrated Ph.D. program
Environmental Systems Engineering Lab.
School of Environmental Science & Engineering
Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
Phone : +82-10-2003-7860
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