Prediction of Surface Soil Bulk Density Based on Grey Theory and BP Neural Network

GUO li-na;FAN Gui-sheng;College of Hydroscience and Engineering,Taiyuan University of Technology;

soil bulk density;;BP neural network;;grey relational theory;;accumulated receiving water;;soil transfer function;;soil physical and chemical parameters

Based on the annual monitoring samples from the fifteen test points in Loess Plateau area of Shanxi Province,a soil transfer function prediction model between surface soil bulk density and soil physicochemical parameters and accumulated receiving water is established by combining grey correlation theory and BP neural network. The results show that the correlation between surface soil bulk density and 7 physicochemical parameters affecting surface soil bulk density is greater than 0.6; Based on BP neural network; it is feasible to select soil silt content,sand content,accumulated receiving water,organic carbon content,volumetric water content and total salt content as input variables to predict the surface soil density,and the predicted values are in good agreement with the measured values; the average relative error of the prediction is 0.41% and the prediction accuracy is high; the average relative error of the test sample prediction results is1.05%,the errors are acceptable totally. The research results can provide new ideas for obtaining soil bulk density in loess region,and provide theoretical support for scientific guidance of farmland farming and irrigation management.