具有机器学习参数化的物理约束的蒸发透气模型优于纯粹的机器学习:域知识的关键作用
Yeonuk Kim1,2, Monica Garcia3, T Andrew Black4
1Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada.
PloS one
|July 23, 2025
概括
基于物理的机器学习提高了陆地蒸发透气 (ET) 估计,特别是在极端条件下. 混合模型,整合物理原理,通过最小化对机器学习参数的灵敏度来减少错误,优于纯ML方法.
科学领域:
- 环境科学 环境科学
- 机器学习 机器学习
- 水文学的水文学
背景情况:
- 陆地蒸发转化 (ET) 对水,能源和碳循环至关重要.
- 纯机器学习 (ML) 模型在ET估计方面扎,特别是在极端条件下.
- 基于物理的ML,特别是混合模型,显示出改善ET精度的希望.
研究的目的:
- 调查混合ET模型性能改进背后的机制.
- 将六种新的混合ET模型与纯ML模型进行比较.
- 为了确定混合ET建模的最佳参数化.
主要方法:
- 开发了六种混合模型,将不同的物理ET配方与随机森林算法集成在一起.
- 训练并比较使用每日ET观测,气象数据和卫星遥感的模型.
- 分析了模型误差 (RMSE) 和对机器学习参数的灵敏度之间的相关性.
主要成果:
- 在ET估计灵敏度和模型误差 (RMSE) 之间发现了强烈的相关性 (r=0.93).
- 对机器学习参数的降低灵敏度将错误传播降到最低,并提高模型性能.
- 最准确的混合模型使用了新的,稳定的经验参数,超越了纯ML和其他混合模型.
结论:
- 传统的参数化可能需要重新评估,以优化物理模型与ML的整合.
- 域名知识对于开发有效的混合模型至关重要.
- 这项研究为推进超越ET估计的混合建模提供了洞察力.
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