参考作物潜在蒸散量的人工神经网络模型研究

    Research of artificial neural network model for reference crop evapotranspiration

    • 摘要: 根据河套灌区多年气象资料和Penman—Monteith法计算得到的参考作物蒸散量(ET0),对影响ET0的主要气象因子进行了回归分析,并比较了以4因子(平均气温、净辐射、相对湿度、2 m处风速)和3因子(平均气温、净辐射、相对湿度)为输入向量,由Penman—Monteith法计算所得ET0为输出向量的BP网络ET0预报模型。研究表明,BP网络可以用于ET0的预报计算,四因子法和三因子法均简便可行,能满足生产的需要。相比之下,四因子法的精度更高。此研究是对传统ET0计算的补充。

       

      Abstract: According to Hetao district long-term meteorology data and reference evapotranspiration (ET0) which were calculated by Penman—Monteith method, main meteorology data affecting ET0 were regressed and analyzed. Based on these, four factors input vector (mean temperature, net radiation, relative humidity and wind speed at 2 m high) BP network forecast model about ET0 were compared with three factors (mean temperature, net radiation, relative humidity) input vector. The research indicated BP network model was suitable for ET0 forecasting, four-factor and three-factor input vector BP network model were both convenient and feasible for forecasting ET0 and could satisfy the needs of production. The precision of four factor input vector network model was higher than three factor input network model. This research is the supplement for traditional ET0 calculation.

       

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