Pedotransfer functions （PTFs） have been developed to estimate soil water retention curves （SWRC） by various techniques. In this study PTFs were developed to estimate the parameters （θs, θr, α and λ） of the B...Pedotransfer functions （PTFs） have been developed to estimate soil water retention curves （SWRC） by various techniques. In this study PTFs were developed to estimate the parameters （θs, θr, α and λ） of the Brooks and Corey model from a data set of 148 samples. Particle and aggregate size distribution fractal parameters （PSDFPs and ASDFPs, respectively） were computed from three fractal models for either particle or aggregate size distribution. The most effective model in each group was determined by sensitivity analysis. Along with the other variables, the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling （mGMDH） and different topologies of artificial neural networks （ANNs）. The architecture of ANNs for parametric PTFs was different regarding the type of ANN, output layer transfer functions and the number of hidden neurons. Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs. The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of ~s- The textural fraction variables in PTF1 for the estimation of a were replaced with PSDFPs in PTF3. The use of ASDFPs as inputs significantly improved a estimates in the model. This result highlights the importance of ASDFPs in developing parametric PTFs. The mCMDH technique performed significantly better than ANNs in most PTFs.展开更多
基金Supported by the Bu Ali Sina University,Iran (No. 65178)
文摘Pedotransfer functions （PTFs） have been developed to estimate soil water retention curves （SWRC） by various techniques. In this study PTFs were developed to estimate the parameters （θs, θr, α and λ） of the Brooks and Corey model from a data set of 148 samples. Particle and aggregate size distribution fractal parameters （PSDFPs and ASDFPs, respectively） were computed from three fractal models for either particle or aggregate size distribution. The most effective model in each group was determined by sensitivity analysis. Along with the other variables, the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling （mGMDH） and different topologies of artificial neural networks （ANNs）. The architecture of ANNs for parametric PTFs was different regarding the type of ANN, output layer transfer functions and the number of hidden neurons. Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs. The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of ~s- The textural fraction variables in PTF1 for the estimation of a were replaced with PSDFPs in PTF3. The use of ASDFPs as inputs significantly improved a estimates in the model. This result highlights the importance of ASDFPs in developing parametric PTFs. The mCMDH technique performed significantly better than ANNs in most PTFs.
