• Title/Summary/Keyword: 관측망

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Performance Comparison of Machine Learning Based on Neural Networks and Statistical Methods for Prediction of Drifter Movement (뜰개 이동 예측을 위한 신경망 및 통계 기반 기계학습 기법의 성능 비교)

  • Lee, Chan-Jae;Kim, Gyoung-Do;Kim, Yong-Hyuk
    • Journal of the Korea Convergence Society
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    • v.8 no.10
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    • pp.45-52
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    • 2017

  • 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.

  • https://doi.org/10.15207/JKCS.2017.8.10.045 인용 PDF KSCI