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Updated: Mar 1, 2026

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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.
Scientific reports
|February 27, 2026
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.
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.
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