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Estimation of Snow Damage and Proposal of Snow Damage Threshold based on Historical Disaster Data

재난통계를 활용한 대설피해 예측 및 대설 피해 적설심 기준 결정 방안

  • 오영록 (호서대학교 건축토목환경공학부 토목공학과) ;
  • 정건희 (호서대학교 건축토목환경공학부 토목공학전공)
  • Received : 2017.01.20
  • Accepted : 2017.03.03
  • Published : 2017.04.01

Abstract

Due to the climate change, natural disaster has been occurred more frequently and the number of snow disasters has been also increased. Therefore, many researches have been conducted to predict the amount of snow damages and to reduce snow damages. In this study, snow damages over last 21 years on the Natural Disaster Report were analyzed. As a result, Chungcheong-do, Jeolla-do, and Gangwon-do have the highest number of snow disasters. The multiple linear regression models were developed using the snow damage data of these three provinces. Daily fresh snow depth, daily maximum, minimum, and average temperatures, and relative humidity were considered as possible inputs for climate factors. Inputs for socio-economic factors were regional area, greenhouse area, farming population, and farming population over 60. Different regression models were developed based on the daily maximum snow depth. As results, the model efficiency considering all damage (including low snow depth) data was very low, however, the model only using the high snow depth (more than 25 cm) has more than 70% of fitness. It is because that, when the snow depth is high, the snow damage is mostly caused by the snow load itself. It is suggested that the 25 cm of snow depth could be used as the snow damage threshold based on this analysis.

최근 세계적인 기상이변으로 인해 자연재해가 빈번하게 발생하고 있으며, 겨울철 대표적인 자연재해인 대설에 의한 재난 발생 빈도도 증가하고 있다. 그러므로 대설 피해 저감이나 대설 피해액 예측에 대한 연구들이 다수 수행되고 있다. 본 연구에서는 과거 22년간 발생했던 대설 피해 사례를 재해연보에서 조사하여 시군구별로 빈도 분석을 하였다. 그 결과 대설 피해 발생 빈도가 높았던 충청도, 전라도, 강원도를 대상으로 대설피해액 예측을 위한 다중회귀모형을 구축하였다. 설명변수로 기상학적 요소인 최심신적설량, 최고기온, 최저기온, 상대습도와 사회 경제적인 요소인 시군구의 면적과 비닐하우스 면적, 농가인구, 60세 이상 농가인구를 선택하여 모형을 구축하였다. 또한 대설 피해를 야기하는 적설심에 대한 분석을 위해 최심신적설심 별 구간을 구분하여 모형을 별도로 구축하였다. 그 결과, 적설심이 낮았던 피해 사례까지를 모두 고려한 경우에는 모형의 예측력이 매우 낮았지만, 피해를 야기한 적설심이 큰 경우만을 분리하여 모형을 구축한 경우에는 70% 이상의 매우 향상된 예측력을 보였다. 이는 적설심이 25 cm 이상 큰 경우에는 적설하중에 의해 설해가 발생할 가능성이 있으며, 이를 대설 피해 기준 적설심이라고 가정할 수 있을 것으로 판단되었다.

Keywords

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