[논문]비주석 재귀신경망 앙상블 모델을 기반으로 한 조위관측소 해수위의 준실시간 이상값 탐지

이은주; 김영택; 김송학; 주호정; 박재훈

doi:10.7850/jkso.2021.26.4.307

[국내논문] 비주석 재귀신경망 앙상블 모델을 기반으로 한 조위관측소 해수위의 준실시간 이상값 탐지
A Non-annotated Recurrent Neural Network Ensemble-based Model for Near-real Time Detection of Erroneous Sea Level Anomaly in Coastal Tide Gauge Observation 원문보기

바다 : 한국해양학회지 = The sea : the journal of the Korean society of oceanography, v.26 no.4, 2021년, pp.307 - 326

이은주 (인하대학교 해양과학과) , 김영택 (국립해양조사원 해양예보과) , 김송학 (인하대학교 해양과학과) , 주호정 (인하대학교 해양과학과) , 박재훈 (인하대학교 해양과학과)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

Keyword

표/그림 (17)

그림 Fig. 1. Bathymetry of the study area and location of five tide gauge stations (BNP: Busan new port, BS: Busan, GJ: Geoje-do, TY: Tongyeong, US: Ulsan).
표 Table 1. Summary of the characteristics of sea level components (BNP: Busan new port, BS: Busan, GJ: Geoje-do, TY: Tongyeong, US: Ulsan)
표 Table 2. Length of dataset at the five tide gauge observatories during 2018－2020 (BNP: Busan new port; BS: Busan; GJ: Geoje-do; TY: Tongyeong; US: Ulsan. Unit: ea.)
그림 Fig. 2. Structure of LSTM model1). The three-dimensional structure consists of batch size, sequence, and feature, while the two-dimensional structure consists of batch size and feature. Question mark indicates flexible batch sizes depending on data length.
표 Table 3. Summary of model verification results at the five tide gauge observatories (BNP: Busan new port; BS: Busan; GJ: Geoje-do; TY: Tongyeong; US: Ulsan)
그림 Fig. 3. Scatter plot of model verification results at the five observatories. The results of the voting are colored, and observations that receive more than 70 percent of the votes are QC targets.
그림 Fig. 4. Time series of validated model results in normal times. The gray and navy solid lines mean the observations and the average value of the model results (filled with blue: 99% confidence interval), respectively. For decision making, fewer (1 to 5) voting and non-voting are represented by green and blue scissors, respectively. Depending on the voting rate with the model, bad, probably bad, and probably good are marked with red, orange, and yellow dots, respectively.
그림 Fig. 5. Time series of validated model results in 2020 oceanic events. Legend is the same as Fig. 4.
그림 Fig. 6. Time series of test data from 2018 to 2019 applied to the validated model trained with 2020 data (in normal times). Legend is the same as Fig. 4.
그림 Fig. 7. Time series of test data from 2018 to 2019 applied to the validated model trained with 2020 data (during the oceanic events). Legend is the same as Fig. 4.
그림 Fig. 8. Test for overfitting cases depending on the year of training data. The horizontal axis represents the training year and the vertical axis represents the test year, and hence the overfitting can be seen in the diagonal components (red boxes; the same year of training and test). Legend is the same as Fig. 4.
그림 Fig. 9. Test for underfitting cases depending on the year of training data. The x-axis represents the training year and the y-axis represents the test year. At very few oceanic events, underfitting may occur, especially in untrained years (black boxes). Legend is the same as Fig. 4.
표 Table 4. Configurations of the case studies for the impact of time step change
표 Table 5. Summary of quality control results for the three model cases
그림 Fig. 10. Time series of Case1 (FC-ANN, time step: 1) model results. Legend is the same as Fig. 4.
그림 Fig. 11. Time series of Case2 (LSTM, time step: ±1 hour) model results. Legend is the same as Fig. 4.
그림 Fig. 12. Quality control results from the three QC methods. The first row is the result of this study, the second row is an example of a 5sigma rule with a combination of the residuals and the time variations of the residuals, and the third row is an example of a 3sigma rule with a 31-minute window. The solid black line represents the quality-treated sea level value. The solid yellow line means the time variation of the residuals, and the dashed gray and yellow line means the 5σ of the residuals and the residual variation, respectively. The red dot is the target of QC, and the green and blue dot is the target of QC by the 5σ limit of residual and residual variations, respectively.

참고문헌 (24)

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