Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
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Prediction of Storm Surge Height Using Synthesized Typhoons and Artificial Intelligence

합성태풍과 인공지능을 활용한 폭풍해일고 예측

  • 엄호식 ((주)지오시스템리서치 해양예보사업부) ;
  • 박종집 ((주)지오시스템리서치 해양예보사업부) ;
  • 정광영 (국립해양조사원 해양과학조사연구실) ;
  • 박영민 ((주)지오시스템리서치 해양예보사업부)
  • Received : 2020.11.27
  • Accepted : 2020.12.28
  • Published : 2020.12.31
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Abstract

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

태풍 내습 시 신속하고 정확한 해일고 예측은, 연안재해 대응에 필수적인 요소이다. 이러한 해일고의 예측을 위해서 기존에는 태풍예측정보를 수치모델에 적용하여 예측자료를 생산하는 것이 대부분 이였다. 이러한 방법은 대용량의 컴퓨팅 자원과 시간이 소요된다는 단점이 있다. 최근에는 인공지능 기반으로 신속하게 예측자료를 생산하는 연구가 다양한 분야에서 진행되고 있으며, 본 연구에서는 인공지능 기반 해일고 예측을 수행하였다. 인공지능 적용을 위해서는 많은 수의 학습자료가 필요하게 되며, 기왕 발생태풍은 개수가 한정되어 있어 본 연구에서는 TCRM(Tropical Cyclone Risk Model)을 통하여 합성태풍을 생성하고, 이를 폭풍해일 모델에 적용하여 해일고 자료를 생성한 후, 학습자료로 활용하였다. 인공지능으로 예측한 해일고와 실제 발생 태풍에 대한 비교 결과, RMSE(Root Mean Square Error)는 0.09 ~ 0.30 m, CC(Correlation Coefficient)는 0.65 ~ 0.94, 최대 해일고의 ARE(Absolute Relative Error)는 1.0 ~ 52.5 %로 분석되었다. 특정 태풍/지점에서는 다소 오차가 크게 나타나고 있으나, 향후 학습자료의 최적화 등을 통하여 정확도를 개선할 수 있을 것으로 기대된다.

Keywords

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