赵西宁, 王万忠, 吴普特, 冯浩, 吴发启. 坡面入渗的人工神经网络模型研究[J]. 农业工程学报, 2004, 20(3): 48-50.
    引用本文: 赵西宁, 王万忠, 吴普特, 冯浩, 吴发启. 坡面入渗的人工神经网络模型研究[J]. 农业工程学报, 2004, 20(3): 48-50.
    Zhao Xining, Wang Wanzhong, Wu Pute, Feng Hao, Wu Faqi. Artificial neural network model for soil infiltration in slope farmland[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2004, 20(3): 48-50.
    Citation: Zhao Xining, Wang Wanzhong, Wu Pute, Feng Hao, Wu Faqi. Artificial neural network model for soil infiltration in slope farmland[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2004, 20(3): 48-50.

    坡面入渗的人工神经网络模型研究

    Artificial neural network model for soil infiltration in slope farmland

    • 摘要: 根据野外人工模拟降雨试验得到的不同耕作措施下坡面土壤入渗实测资料,引入人工神经网络建模方法,建立了不同耕作措施,如等高耕作、人工掏挖、人工锄耕和直线坡条件下坡面入渗BP网络模型,并利用实测资料对网络进行了训练和预测,取得了较好的结果,说明该模型的建立与求解为复杂坡面土壤入渗规律的研究提供了一条新途径。

       

      Abstract: Based on the observed data of the field simulated rainfall experiment for slope farmland in Loess Plateau of China, the paper used method of artificial neural network model, and established back-propagation network model for slope soil infiltration in different tillage measures (contour tillage, artificial digging, artificial hoeing, linear slope). The network model was trained and predicted by using the observed data of the field simulated rainfall experiment. The results showed that back-propagation network model in this paper were reasonable and can be referred as an effective method for studying soil infiltration laws in slope farmland.

       

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