农业气象相关模型与机器学习方法结合研究回顾  

Review of Research on Combining Agricultural Meteorological Models with Machine Learning Methods

作　　者：周东 卢强伟 张瑛 Zhou Dong;Lu Qiangwei;Zhang Ying(Ganzhou Meteorological Bureau,Ganzhou 34100,China;Shanggao County Meteorological Bureau,Shanggao 336400,China;Jiangxi Vocational and Technical College of Information Application,Nanchang 330043,China)

机构地区：[1]赣州市气象局,江西赣州341000 [2]上高县气象局,江西宜春336400 [3]江西信息应用职业技术学院,江西南昌330043

出　　处：《气象与减灾研究》2024年第4期302-308,共7页Meteorology and Disaster Reduction Research

基　　金：江西省重点研发计划项目(编号:20243BBH81009);江西省防灾减灾工程技术研究中心专项(编号:JX2023Z01);赣州市气象防灾减灾科学技术研究开发基金项目“赣州市积温时空变化特征分析”.

摘　　要：为探究机器学习方法在农业气象相关模型领域的应用进展,对机器学习与农业气象相关模型的结合研究进行了回顾与总结。观测设备的快速发展显著提升了数据的可用性和多源性,为机器学习与模型的结合奠定了坚实的数据基础;同时,机器学习理论研究的不断深入推动了算法的优化与创新,为机器学习与模型的结合提供了强有力的算法支持。作为一种高效的数据拟合与分析手段,机器学习在农业气象相关模型领域展现广阔的发展前景。通过机器学习方法,模型的准确性和普适性得到了显著提升,为农业气象研究提供了新的技术路径和方法支撑。To explore the application progress of machine learning methods in the field of agricultural meteorological models,this paper systematically reviewed the integration of machine learning techniques into agricultural meteorological modeling.Research indicated that the rapid development of observation equipment significantly enhanced the availability and diversity of data,laying a solid data foundation for the integration of machine learning and modeling.Meanwhile,the continuous advancement of machine learning theory has driven the optimization and innovation of algorithms,providing strong algorithmic support for the combination of machine learning and modeling.As an efficient method of data fitting and analysis,machine learning demonstrates broad development prospects in the field of agricultural meteorological models.The application of machine learning methods significantly improved the accuracy and generalizability of models,offering new technical pathways and methodological support for agricultural meteorological studies.

关 键 词：农业气象 相关模型 机器学习 研究进展 

分 类 号：S16[农业科学—农业气象学]