Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Evaporative demand drought index forecasting in Busan-Ulsan-Gyeongnam region using machine learning methods

기계학습기법을 이용한 부산-울산-경남 지역의 증발수요 가뭄지수 예측

  • Lee, Okjeong (Water Resources Management Research Center, K-water Research Institute) ;
  • Won, Jeongeun (Division of Earth Environmental System Science (Major of Environmental Engineering). Pukyong National University) ;
  • Seo, Jiyu (Division of Earth Environmental System Science (Major of Environmental Engineering). Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 이옥정 (K-water연구원 유역물관리연구소) ;
  • 원정은 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 서지유 (부경대학교 지구환경시스템과학부 환경공학전공) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2021.05.25
  • Accepted : 2021.06.14
  • Published : 2021.08.31
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Abstract

Drought is a major natural disaster that causes serious social and economic losses. Local drought forecasts can provide important information for drought preparedness. In this study, we propose a new machine learning model that predicts drought by using historical drought indices and meteorological data from 10 sites from 1981 to 2020 in the southeastern part of the Korean Peninsula, Busan-Ulsan-Gyeongnam. Using Bayesian optimization techniques, a hyper-parameter-tuned Random Forest, XGBoost, and Light GBM model were constructed to predict the evaporative demand drought index on a 6-month time scale after 1-month. The model performance was compared by constructing a single site model and a regional model, respectively. In addition, the possibility of improving the model performance was examined by constructing a fine-tuned model using data from a individual site based on the regional model.

가뭄은 심각한 사회적 경제적 손실을 초래하는 주요 자연재해이다. 지역 가뭄 예측은 가뭄 대비에 중요한 정보를 제공할 수 있다. 본 연구에서는 한반도 동남부 부산-울산-경남 지역에서 1981년부터 2020년까지 10개 관측소의 과거 가뭄지수 및 기상 관측자료를 사용하여 가뭄을 예측하는 새로운 기계학습모델을 제안한다. 베이지안 최적화기법을 이용하여 하이퍼 파라미터가 튜닝된 Random Forest, XGBoost, Light GBM 모델을 구축하여 1개월 뒤의 6개월 시간 척도의 증발 수요 가뭄지수를 예측하였다. 단일 지점별 모델과 지역 모델을 각각 구성하여 모델 성능을 비교하였다. 또한 지역 모델을 기반으로 개별 지점의 자료에 대해 미세조정된 모델을 구성하여 모델 성능을 높일 가능성을 살펴보았다.

Keywords

Acknowledgement

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행되었음(NRF-2019R1A2C1003114).

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