审查用于海平面变化建模和预测的机器学习方法
Akeem Shola Ayinde1, Yu Huaming2, Wu Kejian2
1College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China; Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; Department of Marine Meteorology and Climate, Nigerian Institute for Oceanography and Marine Research, PMB, 12729, Victoria Island, Lagos, Nigeria.
The Science of the total environment
|September 23, 2024
概括
机器学习模型,特别是人工神经网络,对预测海平面变化充满希望. 未来的研究应该将这些与基于物理的模型相结合,以实现更好的长期沿海管理.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 海平面变化 (SLC) 是全球气候变化的关键后果,对全球沿海社区构成重大风险.
- 有效的沿海管理和适应战略需要准确和及时预测SLC.
研究的目的:
- 审查和评估开发可靠机器学习 (ML) 模型的方法,用于预测和预测海平面变化.
- 评估不同ML技术的性能,并确定影响模型准确性的关键因素.
主要方法:
- 关于用于海平面变化预测和预测的机器学习方法的综合文献综述.
- 分析各种ML模型,包括人工神经网络 (ANN),深度学习,回归和支持矢量机器.
- 检查输入变量选择和数据分区对模型性能的影响.
主要成果:
- 与更简单的ML方法相比,深度学习模型和混合ANN在短期海平面异常预测方面表现优越.
- 监督学习是普遍存在的,而半监督方法在短期预测中显示出有效性.
- 模型的准确性受到大气,海洋和地质输入变量的选择以及训练/测试数据平衡的影响.
结论:
- 机器学习为海平面变化预测提供了强大的工具,先进的ANN在短期异常预测方面表现出色.
- 未来的研究应该优先考虑将基于物理的一般循环模型 (GCM) 与ML技术相结合,以加强对沿海弹性至关重要的区域长期预测.
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