基于ANN的复垦土壤水分特征曲线的预测研究

为能较容易且更准确地获取复垦土壤水分特征曲线,将易测定的土壤特性如土壤质地、容重和饱和含水量作为输入变量,采用基于bagging算法的神经网络法建立了用于预测土壤水分特征曲线的土壤转换函数法(PTFs)模型,并对徐州矿区复垦土壤的水分特征曲线进行了预测,同时与普通BP算法预测精度进行了比较.研究结果表明所建立的PTFs参数模型具有较高的估计精度,bagging算法均方根预测误差比普通BP算法减少了7.5%～27.0%,说明该模型的建立与求解为复垦土壤水分特征曲线的预测研究提供了一条新途径.

Artificial neural network for predicting water retention curves of reclaimed soils

With the dataset of reclaimed soils in Xuzhou mine area of China,neural networks coupled with bootstrap aggregation were developed to predict the soil water retention curves from routinely and easily determined soil properties such as texture,bulk density and saturated water content.Results indicated good agreement between observed and predicted values.The root mean squared of residuals for predicted values of water content were reduced by 7.5%-27.0% when compared with predicted values using back-propagation algorithm.Use of the developed neural network models is attractive because of improved accuracy and because it permits a considerable degree of flexibility toward available input data.