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The Neuro-m Method for Fitting Neural Network Parametric Pedotransfer Functions

Australian Cotton Cooperative Research Centre, Department of Agricultural Chemistry and Soil Science, Ross St. Building A03, The University of Sydney, NSW 2006, Australia

View larger version (26K): [in a new window]   Fig. 1. The scaled water content Se and scaled potential h of the (a) measured data, and (b) predicted using extended nonlinear regression.

View larger version (37K): [in a new window]   Fig. 2. The structure of a neural network.

View larger version (25K): [in a new window]   Fig. 3. Structure of a neural network predicting the van Genuchten parameters and water retention.

View larger version (13K): [in a new window]   Fig. 4. Akaike's Information Criterion (AIC) as a function of the number of hidden units.

View larger version (38K): [in a new window]   Fig. 5. Measured and predicted water content using Neuroman and Rosetta with four inputs for the Australian prediction, Australian validation, and independent GRIZZLY data sets.

View larger version (16K): [in a new window]   Fig. 6. The mean residuals (MR) plotted against root mean squared residuals (RMSR) using Neuroman (Neu) and Rosetta (Ros) for the Australian prediction (P), Australian validation (V), and independent GRIZZLY (G) data sets.