PERFORMANCE ASSESSMENT OF DATA-DRIVEN MODELS IN ARCTIC SEA ICE PREDICTION
Abstract
Data-driven models are emerging as a brand-new approach in Arctic sea ice prediction. In this study, we perform the effectiveness comparison of different machine learning models in predicting the sea ice extent in the whole Arctic Sea and its subregions: the Bering Sea, the East Siberian Sea, the Chukchi Sea, the Beaufort Sea, and the Central Arctic Sea. The machine learning models used in this study include Ridge Regression, Lasso Regression, Decision Tree, k-Nearest Neighbors, Random Forests, XGBoost, as well as two stacked learning models. Our research reveals that due to the different features of sea ice evolution in different sea areas, these data-driven models generate significantly different prediction performances for sea ice evolution in different seas. In detail, two stacked learning models performed the best in the whole Arctic Sea, the stacked learning model LR_Random Forests performed the best in the Beaufort Sea, Bering Sea, and Chukchi Sea, the Random Forests model performed the best in the Central Arctic Sea, and the Decision Tree model performed the best in the East Siberian Sea.
