Neural network model for reference crop evapotranspiration prediction based on weather forecast
Neural network model for reference crop evapotranspiration prediction based on weather forecast
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DOI：
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发表时间：
2006
影响因子：
2.4
通讯作者：
Li Dao-xi
中科院分区：
文献类型：
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作者：
Li Dao-xi
以参考作物蒸散量（ET_0）的变化过程为时间序列，选取近3天的ET_0数据作为影响因子，建立三层BP神经网络模型进行预测。利用从天气预报中获得的最高、最低和平均日气温、天气指数、年日数和风力等气象数据来改进模型的性能。利用江苏省射阳县的观测数据对模型进行训练和检验。结果表明，该模型能较好地反映ET_0与相关因子之间的关系。
The variation process of reference crop evapotranspiration(ET_0)was regarded as time series and the ET_0 data in passed three days were selected as the influencing factors to establish a BP neural network model with three layers for prediction.The meteorological data obtained from weather forecast,such as maximum,minimum and average daily temperature,weather index,number of days in the year and wind_force,were used to improve the performance of the model.The observation data from Sheyang County,Jiangsu Province,were used to train and test the model.The result shows that the proposed model can well reflect the relationships between ET_0 and relevant factors.