在几内亚湾使用监督机器学习的海平面变化和建模
Akeem Shola Ayinde1,2,3, Huaming Yu4,5, Kejian Wu6,7
1College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China. ayindeas@niomr.gov.ng.
Scientific reports
|December 3, 2023
概括
由于气候变化,几内亚湾的海平面正在上升. 机器学习模型准确地预测这些趋势,帮助沿海适应战略.
科学领域:
- 海洋学 海洋学 海洋学
- 气候科学 气候科学
- 机器学习 机器学习
背景情况:
- 海平面上升对像几内亚湾 (GoG) 这样的低沿海地区构成重大威胁.
- 了解历史海平面变化和影响因素对于有效的沿海规划和减缓至关重要.
研究的目的:
- 分析1993年至2020年间印度政府的平均海平面异常 (MSLA) 趋势.
- 研究海平面变化与海洋/大气强迫之间的联系.
- 评估机器学习模型,以优化海平面预测.
主要方法:
- 在三个不同的时期 (1993-2002,2003-2012,2013-2020) 分析了MSLA趋势.
- 调查年度间海平面变化及其与大规模海洋和大气现象的相关性.
- 监督机器学习技术 (随机森林回归,梯度增强机器) 的性能评估,用于海平面建模.
主要成果:
- 在全国范围内观察到MSLA线性趋势的持续上升,总趋势为88毫米 (1993-2020).
- 2013-2020年期间出现了最高的十年趋势 (38.7毫米),2003-2012年增长了100%.
- 随机森林回归和梯度提升机器在复制跨年海平面图案方面分别达到97%和96%的准确性.
结论:
- 印度群岛的海平面上升正在加速,受到区域物理强迫和大规模气候现象的影响.
- 先进的机器学习模型为预测区域海平面变化提供了高准确度.
- 这些发现对于开发强有力的沿海管理和气候适应战略至关重要.
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