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。
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.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2004年第3期48-50,共3页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家863计划项目(2002AA2Z4051)
关键词
坡面入渗
耕作措施
累积入渗量
BP神经网络
slope soil infiltration
tillage measure
cumulative infiltration
back-propagation network
