中国农业气象 ›› 2023, Vol. 44 ›› Issue (06): 502-512.doi: 10.3969/j.issn.1000-6362.2023.06.005

• 农业生物气象栏目 • 上一篇    下一篇

基于两种数据集构建广东县级尺度龙眼产量模型效果对比

殷美祥,罗瑞婷,朱平,曾钦文,招伟文   

  1. (1.广东省气象服务中心,广州 510640;2.广东省突发事件预警信息发布中心,广州 510640;3.广东省河源市气象局,河源 517000;4.广东省佛山市顺德区气象局,佛山 528300
  • 收稿日期:2023-03-06 出版日期:2023-06-20 发布日期:2023-06-17
  • 通讯作者: 朱平,高级工程师,主要从事气象服务管理、智慧气象服务体系、气象服务数字化转型研究,E-mail:61231442@qq.com E-mail:61231442@qq.com
  • 作者简介:殷美祥,E-mail:646343261@qq.com
  • 基金资助:
    广东省重点领域研发计划项目(2020B0101130021);广东省气象局科技项目(GRMC2022LM02);广东省气象公共服务中心科技项目(2021M14)

Effect Comparison of County-scale Model of Longan Yield in Guangdong Based on Two Datasets

YIN Mei-xiang, LUO Rui-ting, ZHU Ping, ZENG Qin-Wen, ZHAO Wei-Wen   

  1. 1.Guangdong Meteorological Service Center, Guangzhou 510640, China; 2.Guangdong Provincial Emergency Early Warning Release Center, Guangzhou 510640; 3.Heyuan Meteorological Bureau of Guangdong Province,Heyuan 517000; 4.Shunde Meteorological Service, Foshan 528300
  • Received:2023-03-06 Online:2023-06-20 Published:2023-06-17

摘要: 为构建县级尺度龙眼产量动态精细化模拟模型,利用1990-2020年广东省茂名市龙眼生产和国家气象观测站逐日气象数据,分析气象因子对龙眼产量的影响,基于随机森林和逐步回归方法,分别采用不同数据方案建立了化州、高州和信宜龙眼产量动态模拟模型,并进行对比分析。结果表明:广东茂名龙眼产量与气象因子密切相关,最低气温和相对湿度对茂名龙眼产量影响最大,随机森林模型和逐步回归模型入选气象因子个数分别为15个和14个,最大相关系数分别为−0.31和0.43。与多元逐步回归法相比,基于随机森林回归法构建的龙眼产量模拟模型准确率较高,模型决定系数(R2)为0.97,提升了7%,平均绝对误差(MAE)为210.16kg·hm−2,下降了52%,均方根误差(RMSE)为289.62kg·hm−2,下降了46%。引入模拟目标区外相似气候特点地区数据重新构建模型后,随机森林回归构建的龙眼产量模拟模型准确率更高,R2提升了3%,MAE下降了32%,RMSE下降了31%,多元逐步回归法构建的模型模拟结果无显著变化。说明基于随机森林回归法构建龙眼产量模拟模型结果可靠,可满足龙眼气象业务服务精细化需求。

关键词: 产量模拟, 龙眼, 精细化, 随机森林回归

Abstract: In order to construct a county-scale dynamic refined simulation model for longan yield, authors analyze the influence of meteorological factors on longan yield using the longan production data of Maoming from 1990 to 2020 and the daily meteorological data of the national meteorological observatory, establish and carry out the comparative analysis on the dynamic simulation model of longan yield in Huazhou, Gaozhou and Xinyi based on the random forest regression method and stepwise regression method with different data schemes. The results showed that the longan yield in Maoming is closely related to meteorological factors, and the minimum temperature and the relative humidity during the growth period have the greatest influence on the longan yield in Maoming, with 15 and 14 meteorological factors selected respectively, and their maximum correlation coefficients being −0.31 and 0.43, respectively. Compared with the multiple stepwise regression method, the accuracy of longan yield simulation model constructed by the random forest regression method is higher. The model determination coefficient (R2) is 0.97, which increases by 7%, the mean absolute error (MAE) is 210.16kg·ha−1, which decreases by 52%, and the root mean square error (RMSE) is 289.62kg·ha−1, which decreases by 46%. When the data of similar climate characteristic areas outside the simulation target region is introduced, the simulation result of the random forest regression model is significantly improved, with R2 increases by 3%, MAE decreases by 32%, and RMSE decreases by 31%, while the simulation result of the stepwise regression model has no significant change. The longan yield simulation model based on the random forest regression method is reliable, which can meet the demand for refined meteorological service of longan.

Key words: Yield simulation, Longan, Refined simulation, Random forest regression