한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제50권4호
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  • Pages.253-261
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  • 2017
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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앙상블 경험적 모드분해법을 활용한 비정상성 확률분포형의 매개변수 추세 분석에 관한 연구

A study on a tendency of parameters for nonstationary distribution using ensemble empirical mode decomposition method

  • 김한빈 (연세대학교 토목환경공학과) ;
  • 김태림 (연세대학교 토목환경공학과) ;
  • 신홍준 (연세대학교 산학협력단) ;
  • 허준행 (연세대학교 토목환경공학과)
  • Kim, Hanbeen (School of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Taereem (School of Civil and Environmental Engineering, Yonsei University) ;
  • Shin, Hongjoon (University-Industry Foundation, Yonsei University) ;
  • Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei University)
  • 투고 : 2017.02.15
  • 심사 : 2017.03.19
  • 발행 : 2017.04.30
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초록

최근 수문자료에서 비정상성 현상들이 관측됨에 따라 비정상성 빈도해석에 관한 연구들이 활발하게 진행되고 있다. 시간에 따라 변화하는 통계적 특성을 고려하기 위하여 다양한 형태의 비정상성 확률분포형이 제시되고 있으며, 비정상성 매개변수를 추정할 수 있는 다양한 방법들이 연구되고 있는 추세이다. 본 연구에서는 앙상블 경험적 모드분해법을 이용한 비정상성 Gumbel 분포형의 매개변수 추정방법을 제시하고 기존에 비정상성 매개변수 추정방법으로 주로 사용되어온 최우도법과 비교해보고자 하였다. 국내 자료의 적용을 위하여 기상청 지점의 다양한 지속기간에 대해 경향성이 나타나는 연 최대치 강우자료를 사용하였다. 적용 결과 선형적 경향성을 나타내는 자료에 대해서는 두 가지 방법 모두 적절한 모형을 선정하였으나, 2차 곡선 형태의 경향성이 존재하는 자료에 대해서는 앙상블 경험적 모드분해법의 경우에만 이러한 경향성을 반영하는 비정상성 Gumbel 모형을 선정하였다.

A lot of nonstationary frequency analyses have been studied in recent years as the nonstationarity occurs in hydrologic time series data. In nonstationary frequency analysis, various forms of probability distributions have been proposed to consider the time-dependent statistical characteristics of nonstationary data, and various methods for parameter estimation also have been studied. In this study, we aim to introduce a parameter estimation method for nonstationary Gumbel distribution using ensemble empirical mode decomposition (EEMD); and to compare the results with the method of maximum likelihood. Annual maximum rainfall data with a trend observed by Korea Meteorological Administration (KMA) was applied. As a result, both EEMD and the method of maximum likelihood selected an appropriate nonstationary Gumbel distribution for linear trend data, while the EEMD selected more appropriate nonstationary Gumbel distribution than the method of maximum likelihood for quadratic trend data.

키워드

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