한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제8권10호
  • /
  • Pages.45-52
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  • 2017
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  • 2233-4890(pISSN)
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  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
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뜰개 이동 예측을 위한 신경망 및 통계 기반 기계학습 기법의 성능 비교

Performance Comparison of Machine Learning Based on Neural Networks and Statistical Methods for Prediction of Drifter Movement

  • 이찬재 (광운대학교 컴퓨터과학과) ;
  • 김경도 (광운대학교 컴퓨터과학과) ;
  • 김용혁 (광운대학교 컴퓨터과학과)
  • 투고 : 2017.09.05
  • 심사 : 2017.10.20
  • 발행 : 2017.10.28
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초록

뜰개는 해양에서 해수의 특성 및 흐름을 관측하기 위한 장비로서, 해수의 흐름 관측을 이용해 유출유 확산 예측을 위해 사용될 수 있다. 본 논문에서는 관측기관에서 사용하는 뜰개가 특정 시간 간격으로 관측한 바람 및 해수의 특성과 이동경로를 기계학습 기법들을 이용하여 학습시키고 예측하는 모델을 제안한다. 서포트벡터 회귀, 방사기저함수 네트워크, 가우시안 프로세스, 다층 퍼셉트론, 순환신경망을 이용하여 뜰개의 이동경로 예측 방법을 제시한다. 기존 MOHID 수치모델과 비교하여 각 기법별로 4 개의 사례중 3 개에서 성능이 개선되었으며, 가장 좋은 개선율을 보인 기법은 LSTM으로 평균 47.59% 개선되었다. 추후 연구에서는 배깅과 부스팅을 이용하여 가중치를 부여하여 정확도를 개선할 예정이다.

Drifter is an equipment for observing the characteristics of seawater in the ocean, and it can be used to predict effluent oil diffusion and to observe ocean currents. In this paper, we design models or the prediction of drifter trajectory using machine learning. We propose methods for estimating the trajectory of drifter using support vector regression, radial basis function network, Gaussian process, multilayer perceptron, and recurrent neural network. When the propose mothods were compared with the existing MOHID numerical model, performance was improve on three of the four cases. In particular, LSTM, the best performed method, showed the imporvement by 47.59% Future work will improve the accuracy by weighting using bagging and boosting.

키워드

참고문헌

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