Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Correction of Drifter Data Using Recurrent Neural Networks

순환신경망을 이용한 뜰개의 관측 데이터 보정

  • 김경도 (광운대학교 컴퓨터과학과) ;
  • 김용혁 (광운대학교 컴퓨터과학과)
  • Received : 2018.01.26
  • Accepted : 2018.03.20
  • Published : 2018.03.28
Download PDF
⟨ Previous Next ⟩

Abstract

The ocean drifter is a device for observing the ocean weather by floating off the sea surface. The data observed through the drifter is utilized in the ocean weather prediction and oil spill. Observed data may contain incorrect or missing data at the time of observation, and accuracy may be lowered when we use the data. In this paper, we propose a data correction model using recurrent neural networks. We corrected data collected from 7 drifters in 2015 and 8 drifters in 2016, and conducted experiments of drifter moving prediction to reflect the correction results. Experimental results showed that observed data are corrected by 13.9% and improved the performance of the prediction model by 1.4%.

해양 뜰개는 해수면을 떠다니며 해양 기상 등을 관측하는 장비로, 뜰개를 통해 관측한 데이터는 해양 기상 예측, 유류유출 예측 등의 상황에서 활용된다. 관측 데이터는 관측 시에 오측(error data) 또는 결측(missing data)이 발생할 수 있으며, 오측 또는 결측된 데이터가 포함 될 경우, 데이터를 사용하는 모델들의 정확도가 떨어질 수 있다. 본 논문에서는 데이터 보정을 위한 방법으로 순환신경망을 이용한 데이터 보정 모델을 제안한다. 2015년 7개, 2016년 8개의 뜰개를 통해 수집한 해양 데이터를 이용한 보정 실험 결과와 보정 결과를 검증하기 위한 뜰개 이동 예측 실험을 설명하며, 실험 결과, 데이터 보정을 통해 13.9%의 데이터가 보정되었으며, 이동 예측 모델의 성능이 1.4% 향상되는 것을 보였다.

Keywords

References

  1. J. S. Park, K. R. Kang, S. Lee & S-R. Lee. (2013). Observation of the Sea Surface Skin Current Using a GPS-Drifter. Ocean and Polar Research, 35(4), 193-203. DOI : 10.4217/OPR.2013.35.3.193
  2. R. J. Little & D. B. Rubin. (2014). Statistical analysis with missing data. John Wiley & Sons. DOI : 10.1002/9781119013563
  3. J. W. Graham. (2009). Missing data analysis: Making it work in the real world. Annual review of psychology. Annual Review of Psychology, 60, 549-576. DOI : 10.1146/annurev.psych.58.110405.085530
  4. N. F. Schneidewind & H. M. Hoffmann, (1979). An experiment in software error data collection and analysis. IEEE Transactions on Software Engineering, 3, 276-286. DOI : 10.1109/tse.1979.234188
  5. J. H. Ha & Y. H. Kim, (2016). A Survey on Data Correction of Observation and Prediction Using Machine Learning: Preliminary Study for Optimizing Oil Spill Model. International Journal of Applied Engineering Research, 11(16), 8892-8895. DOI : 10.6088/ijaser.
  6. D. E. Rumelhart, G. E. Hinton & R. J. Williams. (1985). Learning Internal Representations by Error Propagation. DTIC Document, Tech. Rep. DOI : 10.21236/ada164453
  7. J. C. Platt. (1999). Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in Kernel Methods. 185-208.
  8. M. K. Lee, S. H. Moon, Y. H. Kim & B. R. Moon. (2014). Correcting Abnormalities in Meteorological Data by Machine Learning. In Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference, 888-893. DOI : 10.1109/smc.2014.6974024
  9. Y. H. Kim, J. H. Ha, Y. Yoon, N. Y. Kim, H. H. Im, S. Sim & R. K. Choi. (2016). Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational intelligence and neuroscience, 2016(4). DOI : 10.1155/2016/9467878
  10. T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis & P. S. Dokopoulos. (2006). Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion, 21(1), 273-284. DOI : 10.1109/tec.2005.847954
  11. T. Guo, Z. Xu, X. Yao, H. Chen, K. Aberer & K. Funaya. (2016). Robust Online Time Series Prediction with Recurrent Neural Networks. In Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference, 816-825. DOI : 10.1109/dsaa.2016.92
  12. G. D. Kim & Y. H. Kim, (2017). A Survey on Oil Spill and Weather Forecast Using Machine Learning Based on Neural Networks and Statistical Methods. Journal of the Korea Convergence Society, 8(10), 1-8. DOI : 10.15207/JKCS.2017.8.10.001
  13. Y. W. Nam & Y. H. Kim, (2018). Prediction of Drifter Trajectory Using Evolutionary Computation. Discrete Dynamics in Nature and Society 2018, 2018, In press.
  14. C. J. Lee, G. D. Kim & Y. H. Kim, (2017). Performance Comparison of Machine Learning Based on Neural Networks and Statistical Methods for Prediction of Drifter Movement. Journal of the Korea Convergence Society, 8(10), 45-52. DOI : 10.15207/JKCS.2017.8.10.045
  15. C. J. Lee & Y. H. Kim, (2017). Prediction of Drifter Trajectory Using an Ensemble Technique. Convergence Research Letter, 3(4), 671-674.