Development and comparison of multiple models for estimating key soil hydraulic properties considering terrain and soil physiochemical properties
To obtain high-precision data of key soil hydraulic properties(saturated hydraulic conductivity(Ks)and field capacity(Fc))in typical humid mountainous areas in southern China,four models were developed for estimating key soil hydraulic properties of the topsoil,including the multiple linear regression(MLR),genetic algorithm-artificial neural network(GA-BP),support vector regression(SVR),and random forest(RF).In addition,three input-variable combination modes were also established with terrain and soil physicochemical properties as inputs that were selected using correlation analysis.Then,four estimation models are compared with the pedotransfer functions(PTFs)to estimate key soil hydraulic properties.These estimation models are selected to predict soil hydraulic properties of the Tunxi Watershed.The results show that the estimation effect of Ks ranked in descending order as RF,SVR,MLR,GA-BP and PTFs,while the results of Fc ranked as SVR,RF,GA-BP,MLR and PTFs.The spatial variations of Ks and Fc in the Tunxi Watershed show a consistency with the spatial variation of elevation,which indicates that there is a close nonlinear relationship between key soil hydraulic properties and elevation in humid mountainous areas.The SVR and RF models are more suitable for the regression analysis of small samples,while the GA-BP model requires larger samples to fully capture the features to achieve good results.
pedotransfer functionsmultiple linear regressionmachine learningsaturated hydraulic conductivityfield capacity
万方数据
维普
