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기계학습기법을 이용한 부산-울산-경남 지역의 증발수요 가뭄지수 예측
Evaporative demand drought index forecasting in Busan-Ulsan-Gyeongnam region using machine learning methods 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.8, 2021년, pp.617 - 628  

이옥정 (K-water연구원 유역물관리연구소) ,  원정은 (부경대학교 지구환경시스템과학부 환경공학전공) ,  서지유 (부경대학교 지구환경시스템과학부 환경공학전공) ,  김상단 (부경대학교 환경공학과)

초록
AI-Helper 아이콘AI-Helper

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.

Abstract AI-Helper 아이콘AI-Helper

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 ...

주제어

표/그림 (12)

참고문헌 (50)

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