베이지안 다중 비교차 분위회귀 분석 기법을 이용한 비정상성 빈도해석 모형 개발

• 영문명: A Development of Nonstationary Frequency Analysis Model using a Bayesian Multiple Non-crossing Quantile Regression Approach
• 발행기관: 한국연안방재학회
• 저자명: 오랑치맥 솜야(Uranchimeg, Sumiya) 김용탁(Kim, Yong-Tak) 권현한(Kwon, Hyun Han)
• 간행물 정보: 『한국연안방재학회 학술발표대회 논문집』2017년 추계학술대회, 142~142쪽, 전체 1쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2017.10.12

국문 초록

Global warming under the influence of climate change and its direct impact on glacial and sea level are known issue. However, there is a lack of research on an indirect impact of climate change such as coastal structure design which is mainly based on a frequency analysis of water level under the stationary assumption, meaning that maximum sea level will not vary significantly over time. In general, stationary assumption does not hold and may not be valid under a changing climate. Therefore, this study aims to develop a novel approach to explore possible distributional changes in annual maximum sea levels (AMSLs) and provide the estimate of design water level for coastal structures using a multiple non-crossing quantile regression based nonstationary frequency analysis within a Bayesian framework. In this study, 20 tide gauge stations, where more than 30 years of hourly records are available, are considered. First, the possible distributional changes in the AMSLs are explored, focusing on the change in the scale and location parameter of the probability distributions. The most of the AMSLs are found to be upward-convergent/divergent pattern in the distribution, and the significance test on distributional changes is then performed. In this study, we confirm that a stationary assumption under the current climate characteristic may lead to underestimation of the design sea level, which results in increase in the failure risk in coastal structures. A detailed discussion on the role of the distribution changes for design water level is provided.

키워드

Annual Maximum Sea Level Design Water Level Distribution Multiple Non-crossing Quantile Regression Nonstationarity