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可解释的机器学习用于预测评级曲线参数,使用美国各地的通道几何和水文属性
Anupal Baruah1, Reihaneh Zarrabi2, Sagy Cohen2
1Department of Geography and Environment, The University of Alabama, Tuscaloosa, USA. abaruah@ua.edu.
Scientific reports
|December 2, 2025
概括
这项研究开发了一种数据驱动的方法来预测美国各地的水文评级曲线. 二级模型为流量估计提供了准确性和广泛适用性的平衡.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化增加了全球洪水事件,需要更好的水文数据来做出决策.
- 水文分级曲线对于将河流阶段转换为流量流量至关重要,影响洪水建模和地形学.
- 功率定律回归有效地模拟了阶段和放电之间的非线性关系.
研究的目的:
- 开发一种基于数据的双层方法,用于预测连续美国 (CONUS) 溪流网络的水文评级曲线参数 (α,β).
- 探索一种统一的解决方案,用于在大型流网络中表示评级曲线.
- 调查道几何和水文气象因素对评级曲线参数的影响.
主要方法:
- 利用水解声学来支持地表水海洋地形学 (HYDRoSWOT),国家水文学 (NHDPlus v2.1) 和 STREAM-CATCHMENT (STREAMCAT) 数据集.
- 我们比较了四种实证模型:多变量回归,极端梯度提升 (XGBoost),随机森林和支持向量回归.
- 开发了两层 XGBoost 模型:一级用于宽度站点,二级用于更广泛的流网络应用.
主要成果:
- 一级XGBoost模型实现了高预测准确性 (R2 = 0.70),但仅限于测量站点.
- 在CONUS.中,Tier-2 XGBoost模型提供了良好的准确度 (R2 = 0.55) 和在NHDPlus流网络中适用的平衡.
- 该研究确定了影响评级曲线参数的关键通道几何和水文气象属性.
结论:
- 开发的双层方法提供了一个可扩展的解决方案,用于在广泛的河流网络中估计水文评级曲线.
- 二级模型提供了一种改善流量数据可用性的实用方法,特别是在数据稀缺的地区.
- 了解评级曲线参数的驱动因素可以提高水文预测和水资源管理的可靠性.
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