Flow regulation for water quality (chlorophyll a) improvement
Date
2010
Authors
Jeong, K.
Kim, D.
Shin, H.
Kim, H.
Cao, H.
Jang, M.
Joo, G.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
International Journal of Environmental Research, 2010; 4(4):713-724
Statement of Responsibility
Jeong, K. S., Kim, D. K., Shin, H. S., Kim, H. W., Cao, H., Jang, M. H. and Joo, G. J.
Conference Name
Abstract
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.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Status unknown