Author
Listed:
- Kang-Young Jung
(Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)
- Sohyun Cho
(Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)
- Seong-Yun Hwang
(Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)
- Yeongjae Lee
(Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)
- Kyunghyun Kim
(Watershed Pollution Load Management Research Division, National Institute of Environmental Research, Incheon 22689, Korea)
- Eun Hye Na
(Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Korea)
Abstract
To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management.
Suggested Citation
Kang-Young Jung & Sohyun Cho & Seong-Yun Hwang & Yeongjae Lee & Kyunghyun Kim & Eun Hye Na, 2020.
"Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification,"
Sustainability, MDPI, vol. 12(21), pages 1-15, November.
Handle:
RePEc:gam:jsusta:v:12:y:2020:i:21:p:9149-:d:439526
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:9149-:d:439526. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.