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Maize Response to Water with BP Neural Network Method Based on Limited Water

机译:基于有限水分的BP神经网络方法对玉米水分响应

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摘要

Ecological environment facing the great task,soil and water heavy loss,unbalanced annual rainfall and water supply and demand in semi-arid areas of the Loess plateau,harvested rainwater is very limited in terms of collecting and storing rainwater.Physiological-biochemical characteristic of water effect on crop yield is quite complex,and quantitative calculation is difficult.Applying strict mathematic at and physical equation is difficult.Professors brings forward many models for crop response to water,but these models have special trait of terrain tract and time domain and can not be used conveniently.There is obviously different model of crop response to water base on limit water which is according to ETmin/ETa defined relative evapotranspiration.Employing relative yield and evapotranspiration can offset effect of apart factor to model,so the paper takes relative yield and evapotranspiration as input and output sample of BP Neural Network,through compared training and analyzing many times,establishing model of maize response to water based on BP Neural Network,and comparing with Jensen model which is used always in China and gained atisfied result.
机译:黄土高原半干旱地区生态环境面临的任务艰巨,水土流失严重,年降水量不平衡,水供需不平衡,雨水收集与储存的集雨量非常有限。水的生理生化特性对作物产量的影响相当复杂,很难进行定量计算。很难应用严格的数学和物理方程式。教授提出了许多作物对水响应的模型,但是这些模型具有地形和时域的特殊性,不能基于ETmin / ETa定义的相对蒸散量,基于极限水的作物对水的响应模型存在明显不同。采用相对产量和蒸散量可以抵消分离因子对模型的影响,因此本文采用相对产量和蒸发蒸腾量作为BP神经网络的输入和输出样本,通过比较训练和分析许多首先,建立了基于BP神经网络的玉米水响应模型,并与国内常用的詹森模型进行了比较,取得了满意的结果。

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