参考作物腾发量预报在线训练深度学习模型
【目的】探究参考作物腾发量（ET0）的实时预报方法。【方法】以浙江省杭州市萧山区2021年4月24日—2023年12月31日的天气预报数据和整点天气实况资料为数据集，分析模型输入数据的预报精度，采用BP神经网络算法构建ET0预报的深度学习模型，并部署至阿里云服务器进行在线训练。【结果】模型的输入数据中，气温预报准确率较高，且最低气温预报精度高于最高气温，天气类型及风力等级预报存在一定误差。模型预报值与实时数据计算得到的标准值相比，预见期内二者变化趋势大致相同，预报精度较高，训练期与测试期准确率最高分别可达到91.56%和84.75%，训练期均方根误差（RMSE）与平均绝对误差（MAE）平均值分...
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| Veröffentlicht in: | Guanʻgai paishui xuebao 2024-01, Vol.43 (12), p.57 |
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| creator | DENG Xuanying LYU Xinwei ZHENG Wenyan ZHENG Shizong ZHANG, Yadong LUO Tongyuan CUI Yuanlai LUO Yufeng |
| description | 【目的】探究参考作物腾发量（ET0）的实时预报方法。【方法】以浙江省杭州市萧山区2021年4月24日—2023年12月31日的天气预报数据和整点天气实况资料为数据集，分析模型输入数据的预报精度，采用BP神经网络算法构建ET0预报的深度学习模型，并部署至阿里云服务器进行在线训练。【结果】模型的输入数据中，气温预报准确率较高，且最低气温预报精度高于最高气温，天气类型及风力等级预报存在一定误差。模型预报值与实时数据计算得到的标准值相比，预见期内二者变化趋势大致相同，预报精度较高，训练期与测试期准确率最高分别可达到91.56%和84.75%，训练期均方根误差（RMSE）与平均绝对误差（MAE）平均值分别为0.828 mm/d和0.667 mm/d，测试期RMSE与MAE平均值分别为1.049 mm/d和0.829 mm/d。【结论】采用公共天气预报数据构建BP模型在线训练，能够实现ET0的实时预报，精度较高且便于运用，可为农业工作者实时灌溉决策提供数据支撑。 |
| doi_str_mv | 10.13522/j.cnki.ggps.2024176 |
| format | Article |
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| subjects | Accuracy Algorithms Back propagation networks Deep learning Evapotranspiration Irrigation Irrigation water Lead time Machine learning Meteorological data Neural networks Online instruction Parameters Real time Root-mean-square errors Training Water management Weather Weather forecasting |
| title | 参考作物腾发量预报在线训练深度学习模型 |
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