摘要
利用多年实测土壤水分资料和气象资料,建立了考虑多个因素如:外界气象因素及土壤特性、作物生长等对土壤墒情影响的BP人工神经网络模型。应用结果表明:所建立的模型具有较好的预测效果;用BP人工神经网络建立土壤墒情预测模型的方法是可行的。
The movement of soil moisture is influenced by multiple factors, such as weather elements, soil characteristics and crop growth, etc. Based on the observation data of soil water content and meteorological data during 1993 to 2001, a back-propagation （BP） neural network model for soil water content forecast is established. The application results show that the predicted values are coincident well with the observed values, and it is feasible to apply the model in predicting soil moisture condition and setting up irrigation plans.
出处
《干旱地区农业研究》
CSCD
北大核心
2005年第5期98-102,共5页
Agricultural Research in the Arid Areas
基金
国家"863"计划项目(2002AA6Z3031)
国家自然科学基金重点项目(50339030)资助
关键词
土壤墒情
BP神经网络
模型
预测
soil moisture
back-propagation neural network
model
forecast
