한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제54권12호
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  • Pages.1285-1294
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  • 2021
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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독립적 자체경보가 가능한 인공지능기반 하천홍수위예측 모형개발

Development of artificial intelligence-based river flood level prediction model capable of independent self-warning

  • 김수영 (한국건설기술연구원 수자원하천연구본부) ;
  • 김형준 (한국건설기술연구원 수자원하천연구본부) ;
  • 윤광석 (한국건설기술연구원 수자원하천연구본부)
  • Kim, Sooyoung (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Hyung-Jun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Yoon, Kwang Seok (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2021.10.31
  • 심사 : 2021.11.24
  • 발행 : 2021.12.31
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초록

최근 전 세계적으로 기후변화의 영향으로 강우량이 집중되고 강우강도가 커지면서 홍수피해의 규모를 증가시키고 있다. 기존에는 관측되지 않았던 규모의 강우가 내리는가 하면 기록적으로 장기간동안 장마가 지속되기도 한다. 특히, 이러한 피해들은 아세안 국가들에 집중되고 있으며, 최근 해수면 상승, 태풍 및 집중호우로 인해 침수가 빈번히 빌생하는 등 아세안 국가 국민들 중 최소 2,000만 명이 영향을 받고 있다. 우리나라도 각종 ODA사업을 통해 국내의 홍수예경보시스템을 아세안 국가에 지원하고 있지만 통신시설이 불안정하여 중앙제어방식만으로는 한계가 있다. 따라서 본 연구에서는 한 개의 관측소에서 수위, 강우의 관측과, 홍수예측, 경보까지 한번에 가능한 관측소를 개발하기 위한 인공지능기반의 홍수예측모형을 개발하였다. 설마천의 전적비교 관측소의 2009년부터 2020년 까지 10분단위 강우와 수위관측자료를 활용하여 선행예보시간 0.5, 1, 2, 3, 6시간에 대해서 학습, 검증, 시험을 수행하였으며 인공지능알고리즘으로는 LSTM을 적용하였다. 연구결과 모든 선행예보시간에 대해 모형적합도 및 오차에서 우수한 결과를 나타냈다. 설마천과 같이 유역규모가 작고 유역경사가 커서 도달시간이 짧은 경우에는 선행예보시간 1시간은 매우 우수한 예측 결과를 나타낼 것으로 판단되며 유역의 규모나 경사에 따라 더 긴 선행예보시간도 가능할 것으로 예상된다.

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

키워드

과제정보

본 연구는 환경부/물관리 연구사업의 지원으로 수행되었음(과제번호 127568).

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