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DOI: https://doi.org/10.4491/eer.2018.245
Short-term algal bloom prediction in Juksan weir using M5P model-tree and extreme learning machine
Hye-Suk Yi1,2, Bomi Lee2, Sangyoung Park2, Keun-Chang Kwak3, and Kwang-Guk An1
1Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Republic of Korea
2K-water Convergence Institute, Korea Water Resources Corporation, Daejeon 34350, Republic of Korea
3Department of Control and Instrumentation Engineering, Chosun University, Gwangju 61452, Republic of Korea
Corresponding Author: Kwang-Guk An ,Tel: +82-42-821-6408 , Fax: +82-42-822-9690, Email: kgan@cnu.ac.kr
Received: July 23, 2018;  Accepted: October 1, 2018.
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ABSTRACT
In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.
Keywords: Algal bloom | Chlorophyll-a | Extreme learning machine | Juksan weir | M5P model tree | Water quality
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