基于人工神经网络的土壤含水量预报模型

土壤水分含量是影响作物生长的重要因素,精确预测技术对水资源的合理利用与管理具有重要的指导意义。土壤水分运动是一个复杂的时间序列系统,其变化与区域气候条件和生态环境密切相关,具有明显的随机性波动。利用人工神经网络的方法对河南驻马店地区的土壤含水量进行预报,利用表层土壤含水量资料计算了一些与深层土壤含水量相关的预报因子,用以建立驻马店地区深层土壤含水量的神经网络预报模型,并应用独立样本进行了初步的模拟预报检验。结果表明,预报模型取得了令人满意的效果,应用神经网络的方法预报深层土壤含水量是可行的。

Forecast Model of Soil Water Content Based on Artificial Neural Network

Soil water content is an important factor affecting crop growth,and the accurate prediction technique is an important guide to reasonable water resources utilization and management.Soil water dynamics is a complex time series system with obviously random fluctuation,closely related to regional climate and ecological environment.A neural network-based scheme for a multivariate analysis of soil water content was presented.Many indices were combined to make long-term forecasts of soil water content in Zhumadian Henan.For soil water content forecasting,the data from water content at low layers was used to train,and validate the network.Output from the network showed relatively satisfying performance with a forecast model in this preliminary study and application of neural network method in prediction of deep soil moisture content was feasible.