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Simulation of Crop Evaportranspiration Based on BP Neural Network Model and Grey Relational Analysis

机译:基于BP神经网络模型和灰色关联分析的作物蒸发蒸腾模拟。

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Crop evaportranspiration was studied with measured data of Kongque river irrigation district in Xinjiang Province based on application of BP neural networks, a sensitivity analysis about crop evaportranspiration was conducted according to each input factor by using default factor method, and the grey relational analysis method was applied to certify the results.The results showed that the artificial neural networks model could express quantitatively the response relationship between crop evaportranspiration and various factors with sufficient high accuracy. Soil moisture and solar radiation were the main sensitive factors for soil water-salt dynamic in this irrigation district, the interaction amongst various factors formed coupling relationship under the complicated condition. The grey relational analysis method could further verify the sensitivity degree amongst various factors. The combination of the above methods provides feasible method for analyzing the rules of crop water comsumption during crop growing season, and it is complement and perfection for the traditional research methods of crop evaportranspiration.
机译:基于BP神经网络,利用新疆孔雀河灌区的实测数据研究了作物的蒸腾作用,采用缺省因子法,根据输入因子对作物的蒸腾量进行了敏感性分析,并应用了灰关联分析法。结果表明,人工神经网络模型可以足够准确地定量表达农作物蒸发蒸腾量与各种因子之间的响应关系。土壤水分和太阳辐射是该灌区土壤水盐动态的主要敏感因素,复杂条件下各种因素之间的相互作用形成耦合关系。灰色关联分析方法可以进一步验证各种因素之间的敏感性程度。上述方法的结合为分析作物生长期的作物耗水规律提供了可行的方法,是对传统作物蒸发蒸腾研究方法的补充和完善。

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