基于粒子群优化小波神经网络模型的春玉米生育阶段干旱预测
基于粒子群优化小波神经网络模型的春玉米生育阶段干旱预测
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CAO Xiujia , GU Jian , MA Ningning , LIU, Yongqi , WANG, Zihao and YIN Guanghua
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Xinxiang City: Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
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Xinxiang City: Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
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【目的】为更好地开展区域性作物生长季气候干旱预测，指导春玉米高效节水补灌生产。【方法】采用皮尔逊相关系数方法选取了与干旱指数最相关的因子，利用阜新市阜蒙县1965—2019年逐日气象数据，探索建立了粒子群算法优化的小波神经网络模型（PSO-WNN），将春玉米不同生育阶段的水分亏缺指数结果进行对比验证模型精度，并利用模型模拟预测未来5 a干旱发生情况。【结果】通过模型验证，春玉米5个生育阶段（播种—出苗阶段、出苗—拔节阶段、拔节—抽雄阶段、抽雄—乳熟阶段、乳熟—成熟阶段）的均方根误差（RMSE）分别为0.041 9、0.017 4、0.048 1、0.029 7、0.042 1，决定系数R2分别为0.840 2、0.985 3、0.899 0、0.957 5、0.917 7，且预测结果与实际干...
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基于粒子群优化小波神经网络模型的春玉米生育阶段干旱预测
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TN_cdi_wanfang_journals_ggps202103017
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_wanfang_journals_ggps202103017
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1672-3317
DOI
10.13522/j.cnki.ggps.2020531
