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Flow Regulation for Water Quality (chlorophyll a) Improvement | ||
| International Journal of Environmental Research | ||
| مقاله 16، دوره 4، شماره 4، بهمن 2010، صفحه 713-724 اصل مقاله (1.09 M) | ||
| شناسه دیجیتال (DOI): 10.22059/ijer.2010.257 | ||
| نویسندگان | ||
| K.S. Jeong1؛ D.K. Kim2؛ H.S. Shin3؛ H.W. Kim4؛ H. Cao5؛ M.H. Jang6؛ G.J. Joo* 1 | ||
| 1Department of Biological Sciences, Pusan National University, Busan 609-735, South Korea | ||
| 2School of Computer Science & Engineering, Seoul National University, Seoul, 151-742, South Korea | ||
| 3School of Civil and Environmental Engineering, Pusan National University, Busan 609-735, South Korea | ||
| 4Department of Environmental Education, Sunchon National University, Suncheon 540-742, South Korea | ||
| 5School of Earth and Environmental Sciences, University of Adelaide, SA 5005, Australia | ||
| 6Department of Biological Education, Kongju National University, Gongju 314-701, South Korea | ||
| چکیده | ||
| In this study a machine learning algorithm was applied in order to develop a predictive model for the changes in phytoplankton biomass (chlorophyll a) in the lower Nakdong River, South Korea. We used a “Hybrid Evolutionary Algorithm (HEA)†which generated model consists of three functions ‘IF-THENELSE’ on the basis of a 15-year, weekly monitored ecological database. We used the average monthly data, 12 years for the training and development of the rule-set model, and the remaining three years of data were used to validate the model performance. Seven hydrological parameters (rainfall, discharge from four multi-purpose dams, the summed dam discharge, and river flow at the study site) were used in the modeling. The HEA selected reasonable parameters among those 7 inputs and optimized the functions for the prediction of phytoplankton biomass during training. The developed model provided accurate predictability on the changes of chlorophyll a (determination coefficients for training data, 0.51; testing data, 0.54). Sensitivity analyses for the model revealed negative relationship between dam discharge and changes in the chlorophyll a concentration. While decreased dam discharge for the testing data was applied; the model returned increased chlorophyll a by 17-95%, and vice versa (a 3-18% decrease). The results indicate the importance of water flow regulation as specific dam discharge is effective to chlorophyll a concentration in the lower Nakdong River. | ||
| کلیدواژه‌ها | ||
| water quality modeling؛ Machine learning؛ Hybrid Evolutionary Algorithm؛ Nakdong River؛ Smart flow control؛ Sensitivity analysis | ||
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