机器学习在氮循环领域的应用研究进展

氮循环是地球圈层中水-土-气-生多介质、多界面的复杂过程，与土壤健康、粮食安全、全球变暖、空气污染、水体质量等环境问题密切相关。近年来，得益于计算机技术的快速发展和海量、多源数据的产生，机器学习迅速成为研究氮素循环强有力的工具。本文系统梳理了机器学习的功能性概念，包括典型开发流程和学习应用场景等；总结了机器学习的典型应用算法，包括经典机器学习（如随机森林、支持向量机等）和深度学习（如卷积神经网络、长短期记忆网络等）；并综述了机器学习在氮循环研究领域的应用研究进展，包括大气、水体、土壤和植物/作物等介质的氮素代谢机制、模拟氮素循环过程及管理氮素流动等。未来基于大数据和机器学习技术的特征工程和模型融合的研究，将会给氮循环领域的数据分析与建模带来巨大变革。同时，将机器学习与基于物理过程的模型相结合解决氮循环过程中的复杂问题，可为服务国家“双碳”战略以及控制全球变暖、空气污染等环境问题提供重要支撑。

Machine Learning in Nitrogen Cycle Research: A review

Nitrogen cycle is a complex process of multi-media and multi-interface between water-soil-atmosphere-biology in the Earth''s sphere, which is closely related to environmental problems such as soil health, food security, global warming, air pollution and water quality. With the rapid development of computer technology and the generation of massive and multi-source data in recent years, machine learning (ML) has rapidly become a powerful tool to study nitrogen cycle. This paper first introduces the functional concepts of ML, including typical development process and learning application scenarios. Then typical application algorithms of ML are summarized, including classical ML (such as random forest, support vector machine, etc.) and deep learning (such as convolutional neural network, long-term and short-term memory network, etc.). In addition, the application research progress of ML in the field of nitrogepn cycle research are reviewed, including nitrogen metabolism mechanism, simulating nitrogen cycle process and managing nitrogen flow in atmosphere, water, soil and plant/crop. In the future, the research of feature engineering and model fusion based on big data and ML technology will bring great changes to data analysis and modeling in the field of nitrogen cycle. Meanwhile, combine ML with process-based models to solve complex problems in the nitrogen cycle, which will provide important support for serving the national "double carbon" strategy and controlling global warming, air pollution and other environmental issues.