RCM 자료와 기계학습을 이용한 북극권 카라-바렌츠 해역의 해빙면적비 예측 = Prediction of Arctic Sea Ice Concentration of Kara-Barents Seas Using RCM Data with Machine Learning

다국어 초록 (Multilingual Abstract)

Arctic sea ice as an indicator of climate change plays an important role in controlling global climate system. Thus, accurate observation and prediction of Sea Ice Concentration (SIC) is essential for understanding global climate change. In this study, we aim to improve the prediction accuracy of SIC by using machine learning and Regional Climate Model (RCM) data for a more robust method and a higher spatial resolution. Using the CORDEX RCM and NASA SIC data between January 1981 and December 2015, we developed three statistical models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Deep Neural Network (DNN) which can deal with the non-linearity problem, respectively. The DNN model showed the best performance among the three models with the significant correlation between the predictive and observed SIC (r=0.811, p-value < 0.01)and the Root Mean Square Error (RMSE) of 0.258. With deeper considerations of the polar fronts and the characteristics of ocean current and tide, the DNN model can be applied for near future prediction of Arctic sea ice changes.

참고문헌 (Reference)

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