以知识为导向的图形机器学习用于空间分布式预测每日排放和出口动态
Jie Yang1, Bin Peng2, Yaji Wang3
1Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Water research
|March 7, 2026
概括
HydroGraphNet是一种新的图形机器学习模型,可以改善农业水域的流量和气预测. 它通过整合物理知识和学习空间依赖,增强了空间概括,特别是在数据稀缺的领域.
科学领域:
- 水文学的水文学
- 水质建模水质建模
- 机器学习 机器学习
背景情况:
- 准确预测河流流和出口对于农业流域管理至关重要.
- 现有的时间深度学习模型 (例如,LSTM) 由于空间依赖性和流程路径的代表性有限,难以进行空间概括,特别是在数据稀缺的区域.
研究的目的:
- 引入HydroGraphNet,一个新的知识导向图形机器学习框架,用于空间分布的水文和生物地质化学预测.
- 在数据稀缺的农业流域中增强深度学习模型的空间概括能力.
主要方法:
- 开发了HydroGraphNet,将基于过程的知识和显式空间学习集成到使用定向图形拓和质量平衡约束的时间建模中.
- 预先训练的HydroGraphNet基于SWAT+ (土壤和水评估工具加) 的合成数据,以改善稀疏监测区域的概括性.
- 在上桑加蒙河流域评估了HydroGraphNet,将其与一次性和分布式LSTM基线进行比较.
主要成果:
- 在SWAT+模拟上进行基准测试时,HydroGraphNet与一次性LSTM基线相比,显著提高了时间和空间外推性能.
- 在与美国地质调查局数据进行了微调后,HydroGraphNet在排放和NO3-N负载方面的NSE (KGE) 测试中平均得分高于基线.
- 归因分析证实了上游流入表示和基于图形的空间学习对于捕捉交叉下流域依赖性的重要性.
结论:
- HydroGraphNet为空间分布式预测提供了一个可概括的框架,推进了水文建模中物理知识和空间学习的整合.
- 该模型表现出强度和过程忠实性,能够重现季节性水文和生物地化学模式.
- 该框架支持数据稀缺的流域的有针对性的水质管理.
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