한국수자원학회논문집 (Journal of Korea Water Resources Association)

한국수자원학회 (Korea Water Resources Association)
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랜덤포레스트 회귀모형을 적용한 도시지역에서의 실시간 침수 예측

Real-time flood prediction applying random forest regression model in urban areas

  • 김현일 (낙동강홍수통제소 예보통제과) ;
  • 이연수 (경북대학교 토목공학과) ;
  • 김병현 (경북대학교 토목공학과)
  • Kim, Hyun Il (Nakdong River Flood Control Office) ;
  • Lee, Yeon Su (Department of Civil Engineering, Kyungpook National University) ;
  • Kim, Byunghyun (Department of Civil Engineering, Kyungpook National University)
  • 투고 : 2021.09.30
  • 심사 : 2021.11.03
  • 발행 : 2021.12.31
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초록

불안정한 기후와 함께 나타나는 국지적 집중호우로 인한 도시 침수는 끊임없이 발생하고 있으나, 강우량을 포함한 기상정보 현황 또는 예보정보를 활용하여 공간적인 도시홍수 예측정보를 제공할 수 있는 체계는 아직 마련되지 못한 상황이다. 공간적인 홍수정보는 하천의 제방, 도시 하수관거의 통수능, 저류지, 펌프시설과 같은 구조물적 대책에 어려움이 있을 시 발생할 수 있는 최악의 홍수상황을 미리 파악함으로써 피해를 최소화하는데 직접적인 영향을 미칠 수 있다. 이에 본 연구에서는 기상청에서 제공되는 강수량, 도시 유역에 대한 2차원 침수해석 결과, 그리고 기계학습 모형 중 하나인 랜덤포레스트 회귀모형을 활용하여 실시간으로 도시유역에 대한 침수지도를 예측할 수 있는 방법론을 제시하고자 한다. 연구유역은 내수침수가 빈번하게 발생하는 울산시 우정태화지구로 선정하였다. 지속시간 6시간의 총강우량 50 mm, 80 mm 그리고 110 mm 대한 랜덤포레스트 회귀분석 예측 침수면적과 검보정된 2차원 물리모형의 침수해석 결과 비교시 각각 63%, 80%, 그리고 67%의 적합도를 보여주어, 빠른 시간안에 발생하는 도시 침수에 대한 대응, 대피를 위한 기초자료로 활용될 수 있을 것으로 판단된다.

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

키워드

참고문헌

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