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可解释的机器学习用于海岸线预测.

Mahmoud Al Najar1, Dennis G Wilson2, Rafael Almar3

  • 1IRIT, Université de Toulouse, Toulouse INP, 2 Rue Charles Camichel, Toulouse, 31000, France.

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PubMed
概括

符号回归为物理学提供可解释的模型,与黑盒机器学习不同. 这项研究将其应用于海岸线预测,从观测数据中揭示特定区域的物理驱动因素.

关键词:
气候 气候 气候 气候 气候沿海风险 沿海风险 沿海风险象征性回归是一种象征性回归.全球范围 全球范围 全球范围

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科学领域:

  • 环境科学 环境科学
  • 地质物理学 地质物理学
  • 计算科学 计算科学

背景情况:

  • 机器学习 (ML) 已经推进了科学建模,但在基于物理的应用中往往缺乏可解释性.
  • 传统的基于物理的模型可能无法在各种环境中进行概括,从而限制了它们的适用性.
  • 符号回归 (SR) 提供了透明的,可解释的数学表达式,适合揭示物理原理.

研究的目的:

  • 展示符号回归在环境科学中的物理建模中的应用.
  • 使用观测数据开发可解释的海岸线预测模型.
  • 为了发现沿海演变的特定区域的物理驱动因素.

主要方法:

  • 使用符号回归演化了一个可解释模型群体.
  • 针对预测准确性和复杂性的优化模型.
  • 利用全球观测数据进行海岸线变化分析.

主要成果:

  • 成功地将符号回归应用到物理建模中,用于海岸线预测.
  • 发现了代表占主导地位的物理驱动因素的特定区域的数学公式.
  • 生成与物理直觉一致的可解释模型.

结论:

  • 符号回归使数据驱动的发现能够在基于物理的领域中实现,同时保持可解释性.
  • 这种方法为跨尺度海岸线变化的物理动态提供了新的见解.
  • 该方法支持理解气候变化和人类活动影响的沿海演变.