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

The Korean Society of Oceanography (한국해양학회)
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A Non-annotated Recurrent Neural Network Ensemble-based Model for Near-real Time Detection of Erroneous Sea Level Anomaly in Coastal Tide Gauge Observation

비주석 재귀신경망 앙상블 모델을 기반으로 한 조위관측소 해수위의 준실시간 이상값 탐지

  • LEE, EUN-JOO (Department of Oceanography, Inha University) ;
  • KIM, YOUNG-TAEG (Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency) ;
  • KIM, SONG-HAK (Department of Oceanography, Inha University) ;
  • JU, HO-JEONG (Department of Oceanography, Inha University) ;
  • PARK, JAE-HUN (Department of Oceanography, Inha University)
  • 이은주 (인하대학교 해양과학과) ;
  • 김영택 (국립해양조사원 해양예보과) ;
  • 김송학 (인하대학교 해양과학과) ;
  • 주호정 (인하대학교 해양과학과) ;
  • 박재훈 (인하대학교 해양과학과)
  • Received : 2021.09.15
  • Accepted : 2021.11.23
  • Published : 2021.11.30
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Abstract

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

상시 관측되는 조위관측소 해수위 자료는 결측값과 오측값을 포함하고 있으며, 그 중 오측 값은 이상값으로 분류되는 전처리 대상이다. 이러한 오측을 제거하기 위해 대표적으로 3𝜎 (three standard deviations) 규칙이 적용되어왔으나, 기상이변 등에 의한 극값이 존재하거나 3𝜎 범위 안에서도 오측이 존재하는 해수위 자료에는 그 적용이 어렵다. 본 연구에서 설계된 모델은 오측에 대한 사전 정보가 필요하지 않은 비주석 학습으로 구성되며, 재귀신경망과 앙상블 기법을 이용함으로써 실시간으로 수집되는 해수위 자료가 오측일 가능성을 발생한지 20분 이내로 제시한다. 검증이 완료된 모델은 평시 및 기상이변시의 정상값과 오측값을 잘 분리하며, 학습이 이뤄지지 않은 연도의 해수위 자료에서도 이상값 탐지가 가능함을 확인하였다. 본 연구의 관측 이상치 탐지 알고리즘은 조위관측소 해수위에 국한되지 않고 다양한 해양 및 대기자료의 이상치 탐지 인공신경망 모델에 확장 적용할 수 있다.

Keywords

Acknowledgement

본 연구는 국립해양조사원의 「2021년 해양예보정보 종합분석 및 특화 해양예보」 사업의 지원을 받아 수행되었습니다.

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