The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY (한국해양학회지:바다)

The Korean Society of Oceanography (한국해양학회)
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Classification and Analysis of Korea Coastal Flooding Using Machine Learning Algorithm

기계학습 알고리즘에 기반한 국내 해수범람 유형 분류 및 분석

  • Received : 2020.03.23
  • Accepted : 2020.12.03
  • Published : 2021.02.28
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Abstract

In this study, Information for the case of seawater flooding and observation data over a period of 10 years (2009~2018) was collected. Using machine learning algorithms, the characteristics of the types of seawater flooding and observations by type were classified. Information for the case of seawater flooding was collected from the reports of the Korea Hydrographic and Oceanographic Agency (KHOA) and the Korea Land and Geospatial Informatics Corporation. Observation data for ocean and meteorological were collected from the KHOA and the Korea Meteorological Agency (KMA). The classification of seawater flooding incidence types is largely categorized into four types, and into 5 development types through combination of 4 types. These types were able to distinguish the types of seawater flooding according to the marine weather environment. The main characteristics of each was classified into the following groups: tidal movement, low pressure system, strong wind, and typhoon. Besides, in consideration of the geographical characteristics of the ocean, the thresholds of ocean factors for seawater flooding by region and type were derived.

최근 10년(2009년~2018년)간의 해수범람 기록정보와 해양 및 해양기상 관측정보를 수집하고 기계학습 알고리즘을 3종을 종합·활용해 해수범람 유형과 유형별 관측정보의 특징을 분류하였다. 해수범람의 기록정보는 국립해양조사원의 침수조사 보고서와 국토정보공사의 침수흔적도를 통해 수집하였으며 해양 및 해양기상관측 정보는 국립해양조사원과 기상청의 부이, 관측소 정보를 수집하였다. 해수범람 발생 유형 분류는 크게 4개의 유형으로 분류되며 4개의 유형의 조합을 통해 5개의 발생 유형으로 분류하였다. 이 유형은 해양기상 환경에 따라 해수범람의 발생 유형을 구분할 수 있었다. 유형별 주요 특징은 대조기, 저기압, 강풍, 태풍으로 구분되었다. 또한, 지리적인 해양특성을 고려하여 지역 및 유형별 해수범람 발생 판단을 위한 해양요소 임계치를 도출하였다.

Keywords

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