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Updated: Jul 6, 2025

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以知识为导向的机器学习可以改善农业生态系统中的碳循环量化

Licheng Liu¹, Wang Zhou^2,3, Kaiyu Guan^4,5,6,7

¹Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN, 55108, USA.

Nature communications

|January 8, 2024

概括

这项研究引入了一个新的知识引导机器学习 (KGML) 框架,以准确量化农业生态系统的碳循环. KGML改善了对土壤有机碳变化的预测,有助于减缓气候变化和可持续农业.

科学领域:

地球系统科学地球系统科学
农业科学农业科学
机器学习机器学习

背景情况:

量化农业生态系统的碳循环对于减缓气候变化和可持续粮食生产至关重要.
现有的模型 (基于流程和数据驱动) 面临不确定性,因为复杂的流程和有限的观察数据.
需要在与决策相关的尺度上进行准确的量化.

研究的目的:

开发和验证一个以知识为导向的机器学习 (KGML) 框架,以改善农业生态系统中的碳循环量化.
为了解决传统建模方法固有的预测不确定性.
提高土壤有机碳变化评估的空间分辨率.

主要方法:

基于过程的模型的知识与高分辨率遥感数据和机器学习 (ML) 的整合.
开发一个混合KGML框架.
在美国玉米带应用和测试KGML框架.

主要成果:

在传统的基于过程和黑子ML模型中,KGML框架表现出优越的性能.
在量化土壤有机碳变化时,KGML实现了显著更高的空间细节 (86%高于传统方法).
该研究提供了一种加强KGML和概括混合建模方法的协议.

结论:

KGML提供了一种强大的方法来减少农业生态系统碳循环建模中的不确定性.
使用KGML,可以对土壤有机碳动态进行高分辨率的评估.
开发的框架可以被通用化用于预测复杂的地球系统动态.

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