All Issue

2021 Vol.26, Issue 4 Preview Page

Article

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

EUN-JOO LEE1
,
YOUNG-TAEG KIM2
,
SONG-HAK KIM3
,
HO-JEONG JU3
,
JAE-HUN PARK4*
이 은주1
,
김 영택2
,
김 송학3
,
주 호정3
,
박 재훈4*
1Ph.D. Student, Department of Oceanography, Inha University, Incheon 22212, Korea
2Senior Researcher, Oceanographic Forecast Division, Korea Hydrographic and Oceanographic Agency, Busan 49111, Korea
3M.S. Student, Department of Oceanography, Inha University, Incheon 22212, Korea
4Professor, Department of Oceanography, Inha University, Incheon 22212, Korea
1인하대학교 해양과학과 박사과정
2국립해양조사원 해양예보과 해양수산연구사
3인하대학교 해양과학과 석사과정
4인하대학교 해양과학과 교수
*Corresponding Author
30 November 2021. pp. 307-326
PDF XML Citation
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.

Keywords
Sea level observation
Abnormal value detection
RNN
LSTM
Ensemble

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 사전정보가 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.

References
1
국립해양조사원(KHOA), 2021. "바다누리 해양정보 서비스". http://www.khoa.go.kr/oceangrid/gis/category/reference/distribution.
2
김마가, 최진용, 방재홍, 이재주, 2019. 임계치 모형과 인공신경망 모형을 이용한 실시간 저수위 수위자료의 이상치 탐지. 한국농공학회, 61(1): 107-120.
3
Bell, C., J.M. Vassie and P.L. Woodworth, 1999. POL/PSMSL Tidal Analysis Software Kit 2000 (TASK-2000). edited by 2000UKCCMS Proudman Oceanographic Laboratory Permanent Service for Mean Sea Level.
4
Castelão, G.P., 2021. A machine learning approach to quality control oceanographic data. Computers & Geosciences, 155. 10.1016/j.cageo.2021.104803
5
Fourrier, M., L. Coppola, H. Claustre, F. D'Ortenzio, R. Sauzède and J.-P. Gattuso, 2020. A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED. Frontiers in Marine Science 7. 10.3389/fmars.2020.00620
6
Goyal, S., T. Jain and Dr. N. Tyagi, 2018. Artificial Neural Network: A Review and its Application in Managing Water Quality Control. International Journal of Scientific Development and Research, 3(6).
7
Guan, Y. and T. Plötz, 2017. Ensembles of Deep LSTM Learners for Activity Recognition Using Wearables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2): 1-28. 10.1145/3090076
8
Hochreiter, S. and J. Schmidhuber, 1997. Long Short-Term Memory. Neural Computation, 9(8): 1735-1780. 10.1162/neco.1997.9.8.17359377276
9
Just, A.C., M.M. De Carli, A. Shtein, M. Dorman, A. Lyapustin and I. Kloog, 2018. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA. Remote Sens (Basel), 10(5). 10.3390/rs1005080331057954PMC6497138
10
Kim, H.J., S.M. Park, B.J. Choi, S.H. Moon and Y.H. Kim, 2020. Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning. Comput Intell Neurosci, 2020: 7980434. 10.1155/2020/798043432256552PMC7086422
11
Linares-Rodriguez, A., J.A. Ruiz-Arias, D. Pozo-Vazquez and J. Tovar-Pescador, 2013. An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images. Energy, 61: 636-645. 10.1016/j.energy.2013.09.008
12
Liu, J., J. Gu, H. Li and K.H. Carlson, 2020. Machine learning and transport simulations for groundwater anomaly detection. Journal of Computational and Applied Mathematics, 380. 10.1016/j.cam.2020.112982
13
López-Lineros, M., J. Estévez, J.V. Giráldez and A. Madueño, 2014. A new quality control procedure based on non-linear autoregressive neural network for validating raw river stage data. Journal of Hydrology, 510: 103-109. 10.1016/j.jhydrol.2013.12.026
14
Miau, S. and W.-H. Hung, 2020. River Flooding Forecasting and Anomaly Detection Based on Deep Learning. IEEE Access 8:198384-198402. 10.1109/ACCESS.2020.3034875
15
Murray, M.T., 1964. A General Method for the Analysis of Hourly Heights of Tide. International Hydrographic Review, 41(2): 91-101.
16
Omar, S., A. Ngadi and H.H. Jebur, 2013. Machine Learning Techniques for Anomaly Detection: An Overview. International Journal of Computer Applications, 79(2). 10.5120/13715-1478
17
Opitz, D. and R. Maclin, 1999. Popular Ensemble Learning: An Empirical Study. Journal of Artificial Intelligence Research, 11: 169-98. 10.1613/jair.614
18
Pastore, V.P., T.G. Zimmerman, S.K. Biswas and S. Bianco, 2020. Annotation-free learning of plankton for classification and anomaly detection. Sci Rep, 10(1): 12142. 10.1038/s41598-020-68662-332699302PMC7376023
19
Russell, S. and P. Norvig, 2010. Artificial Intelligence: A Modern Approach, Third Edition. Upper Saddle River, New Jersey: Prentice Hall, c2010.
20
Saini, S. and Preeti, 2015. Advance Applications of Artificial Neural Network. Journal of Basic and Applied Engineering Research, 2(11): 1001-1005.
21
Sciuto, G., B. Bonaccorso, A. Cancelliere and G. Rossi, 2009. Quality control of daily rainfall data with neural networks. Journal of Hydrology, 364(1-2): 13-22. 10.1016/j.jhydrol.2008.10.008
22
UNESCO (United Nations Educational, Scientific and Cultural Organization). 2013. Ocean Data Standards, Vol.3: Recommendation for a Quality Flag Scheme for the Exchange of Oceanographic and Marine Meteorological Data. (IOC Manuals and Guides, 54, Vol. 3.).
23
Wang, Y., L. Han, W. Liu, S. Yang and Y. Gao, 2019. Study on wavelet neural network based anomaly detection in ocean observing data series. Ocean Engineering, 186. 10.1016/j.oceaneng.2019.106129
24
Yu, J.B. and L.F. Xi, 2009. A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Systems with Applications, 36(1): 909-921. 10.1016/j.eswa.2007.10.003
Information
  • Publisher :The Korean Society of Oceanography
  • Publisher(Ko) :한국해양학회
  • Journal Title :The Sea Journal of the Korean Society of Oceanography
  • Journal Title(Ko) :한국해양학회지 바다
  • Volume : 26
  • No :4
  • Pages :307-326
  • Received Date : 2021-09-15
  • Revised Date : 2021-11-22
  • Accepted Date : 2021-11-23
  • DOI :https://doi.org/10.7850/jkso.2021.26.4.307
Journal Informaiton The Sea Journal of the Korean Society of Oceanography The Sea Journal of the Korean Society of Oceanography
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close