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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.3
- 수록면
- 331 - 340 (10page)
- DOI
- 10.7465/jkdi.2023.34.2.331
이용수
초록· 키워드
오류제보하기
Recent climate disasters such as heat waves and heavy rains have emerged in the United States. These climate disasters are mainly caused by melting sea ice, an important indicator of maintaining the global average temperature. As sea ice continues to decrease due to global warming, this is the time when a strategy to respond to climate disasters caused by a decrease in the sea ice extent is more necessary than ever. As an aid to this, this study provides predictive modeling using a tree-based machine learning approach to predict the decrease in the Arctic sea ice extent. Specifically, the prediction is accomplished through three procedures: First, to impute the missing values of the Arctic sea ice extent, an imputation method using a Kalman smoothing technique is implemented. In addition, various features including the Fourier terms representing a seasonal variation are extracted and generated. Finally, to resolve the drawback of the tree-based machine learning techniques which cannot capture trends, a hybrid strategy based on the combination of the statistical and tree-based machine learning techniques is used. The provided hybrid strategy is thought to be a very good guideline for applying a tree-based machine learning technique to the Arctic sea ice extent.
목차
Abstract
1. Introduction
2. Methodology
3. Analysis
4. Conclusion
References
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UCI(KEPA) : I410-ECN-0101-2023-041-001322931