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다중회귀분석을 이용한 낙동강 하류의 Chlorophyll-a 농도와 복합 영향인자들의 상관관계 분석

Evaluation of Correlation between Chlorophyll-a and Multiple Parameters by Multiple Linear Regression Analysis

  • 임지성 (부산대학교 사회환경시스템공학부) ;
  • 김영우 (부산대학교 사회환경시스템공학부) ;
  • 이재호 (부산대학교 사회환경시스템공학부) ;
  • 박태주 (부산대학교 사회환경시스템공학부) ;
  • 변임규 (부산대학교 환경기술.산업개발연구소)
  • Lim, Ji-Sung (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Kim, Young-Woo (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Lee, Jae-Ho (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Park, Tae-Joo (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Byun, Im-Gyu (Institute for Environmental Technology and Industry, Pusan National University)
  • 투고 : 2015.03.23
  • 심사 : 2015.05.11
  • 발행 : 2015.05.31

초록

1998년부터 2008년까지 낙동강 물금지점의 수질, 수리 및 기상 인자를 통해 조류발생의 주요 영향인자를 평가하고 chlorophyll-a (chl-a) 예측 모델을 도출하였다. 분석 전 데이터의 신뢰도 향상을 위해 이상치를 제거하는 전처리 과정을 수행하였다. 국내 태풍영향기간을 산정하여 태풍영향기간에 포함된 데이터는 제외를 하였고 관리도 기법을 적용하여 이상치를 제거하였다. 이후 분석과정은 Method 1과 2 두 가지 방법으로 나뉘었다. Method 1은 전처리 과정을 거친 17개 항목의 데이터를 직접 이용하여 chl-a 예측모형을 도출 하였고, Method 2는 전처리 후 주요 영향인자를 도출하여 이를 통해 모형을 개발하였다. Method 2의 상관관계 분석결과 물금지역의 chl-a에 대한 주요영향인자는 수온, pH, DO, BOD, COD, T-N, $NO_3-N$, $PO_4-P$, 유량, 유속 및 수심으로 나타났다. Method 1, 2에 의한 chl-a 회귀모형은 각각 $R^2$ 값이 0.799 및 0.790의 높은 유의성을 나타내었고 2009~2010년의 데이터로 검증하였다. Method 1에 의한 chl-a 예측모형의 training period와 validation period의 RMSE 값은 각각 20.912와 24.423으로 나타났으며 Method 2를 통한 모델은 각각 21.422 및 26.277을 나타냈다. 예측 모형 도출 결과 Method 1 및 2 모두 BOD, DO 및 $PO_4-P$ 세 가지 인자가 주요한 역할을 한 것을 볼 때 향후 물금 지점의 조류 발생은 BOD, DO 및 $PO_4-P$를 중점적으로 관리할 필요가 있다고 판단된다.

In this study, Chlorophyll-a (chl-a) prediction model and multiple parameters affecting algae occurrence in Mulgeum site were evaluated by statistical analysis using water quality, hydraulic and climate data at Mulgeum site (1998~2008). Before the analysis, control chart method and effect period of typhoon were adopted for improving reliability of the data. After data preprocessing step two methods were used in this study. In method 1, chl-a prediction model was developed using preprocessed data. Another model was developed by Method 2 using significant parameters affecting chl-a after data preprocessing step. As a result of correlation analysis, water temperature, pH, DO, BOD, COD, T-N, $NO_3-N$, $PO_4-P$, flow rate, flow velocity and water depth were revealed as significant multiple parameters affecting chl-a concentration. Chl-a prediction model from Method 1 and 2 showed high $R^2$ value with 0.799 and 0.790 respectively. Validation for each prediction model was conducted with the data from 2009 to 2010. Training period and validation period of Method 1 showed 20.912 and 24.423 respectively. And Method 2 showed 21.422 and 26.277 in each period. Especially BOD, DO and $PO_4-P$ played important role in both model. So it is considered that analysis of algae occurrence at Mulgeum site need to focus on BOD, DO and $PO_4-P$.

키워드

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피인용 문헌

  1. Analysis of Water Quality factor and Correlation between Water Quality and Chl-a in Middle and Downstream Weir Section of Nakdong River vol.39, pp.2, 2017, https://doi.org/10.4491/KSEE.2017.39.2.89
  2. Spatiotemporal and Longitudinal Variability of Hydro-meteorology, Basic Water Quality and Dominant Algal Assemblages in the Eight Weir Pools of Regulated River (Nakdong) vol.51, pp.4, 2018, https://doi.org/10.11614/KSL.2018.51.4.268
  3. A Study on the Origin of Organic Matter in Seawater in Korean Estuaries Using Chemical Oxygen Demand vol.24, pp.6, 2018, https://doi.org/10.7837/kosomes.2018.24.6.735