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http://dx.doi.org/10.3741/JKWRA.2013.46.7.697

Study on Establishing Algal Bloom Forecasting Models Using the Artificial Neural Network  

Kim, Mi Eun (Dept. of Civil Engrg., Pusan National Univ.)
Shin, Hyun Suk (Dept. of Civil Engrg., Pusan National Univ.)
Publication Information
Journal of Korea Water Resources Association / v.46, no.7, 2013 , pp. 697-706 More about this Journal
Abstract
In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.
Keywords
artificial neural network; water quality forecasting; algal bloom; chlorophyll-a;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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