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2022 Vol.27, Issue 2 Preview Page

Article

WQI Class Prediction of Sihwa Lake Using Machine Learning-Based Models

기계학습 기반 모델을 활용한 시화호의 수질평가지수 등급 예측

SOO BIN KIM1
,
JAE SEONG LEE23*
,
KYUNG TAE KIM2
김 수빈1
,
이 재성23*
,
김 경태2
1Research Specialist, Marine Environmental Research Center, Korea Institute of Ocean Science & Technology (KIOST), Busan 49111, Korea
2Principal Research Scientist, Marine Environmental Research Center, Korea Institute of Ocean Science & Technology (KIOST), Busan 49111, Korea
3Adjunct Professor, Dept. of Convergence Study on the Ocean Science & Technology, School of Ocean Science & Technology (OST), Busan 49112, Korea
1한국해양과학기술원 해양환경연구센터 기술원
2한국해양과학기술원 해양환경연구센터 책임연구원
3해양과학기술전문대학원 해양과학기술융합과 겸임교수
*Corresponding Author
31 May 2022. pp. 71-86
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Abstract

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DOBot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DINSur) and dissolved inorganic phosphorus (DIPSur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

Keywords
Water quality index
Machine learning
Sihwa Lake
Adaptive boosting (AdaBoost)
Tree-based pipeline optimization (TPOT)

해양환경을 정량적으로 평가하기 위해 수질평가지수(water quality index, WQI)가 사용되고 있다. 우리나라는 해양수산부고시 해양환경기준에 따라 WQI를 5개 등급으로 구분하여 수질을 평가한다. 하지만, 방대한 수질 조사 자료에 대한 WQI 계산은 복잡하고 많은 시간이 요구된다. 이 연구는 기존의 조사된 수질 자료를 활용하여 WQI 등급을 예측할 수 있는 기계학습(machine learning, ML) 기반의 모델을 제안하고자 한다. 특별관리해역인 시화호를 모델링 지역으로 선정하였다. AdaBoost와 TPOT 알고리즘을 모델 훈련을 위해 사용하였으며, 분류 모델 평가 지표(정확도, 정밀도, F1, Log loss)로 모델 성능을 평가하였다. 훈련하기 전, 각 알고리즘 모델의 최적 입력자료 조합을 탐색하기 위해 변수 중요도와 민감도 분석을 수행하였다. 그 결과 저층 용존산소(dissolved oxygen, DO)는 모델의 성능에서 가장 중요한 인자였다. 반면, 표층 용존무기질소(dissolved inorganic nitrogen, DIN)와 표층 용존무기인(dissolved inorganic phosphorus, DIP)은 상대적으로 영향이 적었다. 한편, 최적 모델의 시공간적 민감도와 WQI 등급 별 민감도를 비교한 결과 각 조사 정점 및 시기, 등급 별 모델의 예측 성능이 상이하였다. 결론적으로 TPOT 알고리즘이 모든 입력자료 조합에서 성능이 더 우수하여 충분한 자료로 훈련된 최적 모델은 새로운 수질 조사 자료의 WQI 등급을 정확하게 분류할 수 있을 거라 판단된다.

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Information
  • Publisher :The Korean Society of Oceanography
  • Publisher(Ko) :한국해양학회
  • Journal Title :The Sea Journal of the Korean Society of Oceanography
  • Journal Title(Ko) :한국해양학회지 바다
  • Volume : 27
  • No :2
  • Pages :71-86
  • Received Date : 2022-05-03
  • Revised Date : 2022-05-14
  • Accepted Date : 2022-05-16
  • DOI :https://doi.org/10.7850/jkso.2022.27.2.071
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