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2020 Vol.13, Issue 4 Preview Page

Original Article

Development of Snow Depth Frequency Analysis Model Based on A Generalized Mixture Distribution with Threshold

최심신적설량 빈도분석을 위한 임계값을 가지는 일반화된 혼합분포모형 개발

Ho Jun Kim1
,
Jang-Gyeong Kim2
,
Hyun-Han Kwon3*
김 호준1
,
김 장경2
,
권 현한3*
1Ph.D Course, Dept. of Civil and Environmental Engineering, Sejong University
2Ph.D, Bayesian Works Research Institute
3Professor, Dept. of Civil and Environmental Engineering, Sejong University
1세종대학교 건설환경공학과 박사과정
2베이지안웍스 공학박사
3세종대학교 건설환경공학과 교수
*Corresponding Author
31 December 2020. pp. 25-36
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Abstract

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (δ) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

Keywords
Mixture distribution function
Maximum snow depth
Bayesian model
Frequency analysis

기후변화로 인해 다양한 자연재해의 발생빈도 및 강도가 증가하고 있으며, 이를 대비하기 위하여 행정안전부에서 가뭄과 대설까지 포함한 자연재해저감 종합계획을 발표하였다. 강설량은 기온과 지형적 요인의 영향을 크게 받는다. 산악지형이 많은 강원도는 강설량이 많아 큰 적설량이 관측되지만, 겨울철 평균 온도가 상대적으로 높은 남부지방은 적설량이 작다. 무강설과 결측으로 인해 관측값에 0이 포함된 경우가 존재한다. 자료에 포함된 0은 통계적으로 민감하게 작용하며, 최적 확률분포 선정과 매개변수 추정이 어려워지는 문제점이 발생한다. 본 연구에서는 창원, 통영, 진주 관측소의 최심신적설에 대해 혼합분포를 적용하여 0을 구분하였고, 0에 근사한 값을 나누는 기준인 임계값을 매개변수 δ로 가정함으로써 무적설 기준을 자동으로 모형에서 추정하도록 하였다. Bayesian기법 활용하여 혼합분포모형의 매개변수를 추정하였고, 산정된 빈도별 확률적설심의 불확실성을 정량화하였다. 대관령 지점과 비교한 결과, 본 연구의 혼합분포모형은 적설량이 적은 지점에 대해 적용성이 우수한 것으로 평가되었다.

키워드
혼합분포함수
최심신적설
Bayesian 모형
빈도분석
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Information
  • Publisher :Korean Society of Disaster and Security
  • Publisher(Ko) :한국방재안전학회
  • Journal Title :Journal of Korean Society of Disaster and Security
  • Journal Title(Ko) :한국방재안전학회 논문집
  • Volume : 13
  • No :4
  • Pages :25-36
  • Received Date : 2020-09-18
  • Revised Date : 2020-10-16
  • Accepted Date : 2020-11-10
  • DOI :https://doi.org/10.21729/ksds.2020.13.4.25
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