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.

1. 서론
 2. 연구지역 및 데이터
  1) 연구지역
  2) 해빙면적비 자료
  3) 기후자료
 3. 분석방법
  1) 다중선형회귀
  2) Support Vector Machine
  3) 심층신경망
 4. 결과 및 고찰
 5. 결론
 References

• 김지원(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Ji-Won Kim
• 김광진(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Kwang-Jin Kim
• 이수진(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Soo-Jin Lee
• 김영호(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Yeong-Ho Kim
• 안지혜(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Ji-Hye Ahn
• 이양원(부경대학교 지구환경시스템과학부 공간정보공학전공, Department of Spatial Information Engineering, Pukyong National University) | Yang-Won Lee Correspondence