Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

Kang-Young Jung, Sohyun Cho, Seong-Yun Hwang, Yeongjae Lee, Kyunghyun Kim and Eun Hye Na
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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

Sustainability, 2020, vol. 12, issue 21, 1-15

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.

Keywords: self-organizing maps; neural network model; grade classification; Nakdong river; tributary; management priority (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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