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Soil Science Society of America Journal Abstract - DIVISION S-1—SOIL PHYSICS

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


This article in SSSAJ

  1. Vol. 66 No. 2, p. 352-361
    Received: Mar 6, 2001

    * Corresponding author(s): budiman@acss.usyd.edu.au
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  1. Budiman Minasny * and
  2. Alex. B. McBratney
  1. Australian Cotton Cooperative Research Centre, Department of Agricultural Chemistry and Soil Science, Ross St. Building A03, The University of Sydney, NSW 2006, Australia


Parametric pedotransfer functions (PTFs), which predict parameters of a model from basic soil properties are useful in deriving continuous functions of soil properties, such as water retention curves. The common method for deriving parametric water retention PTFs involves estimating the parameters of a soil hydraulic model by fitting the model to the data, and then forming empirical relationships between basic soil properties and parameters. The latter step usually utilizes multiple linear regression or artificial neural networks. Neural network analysis is a powerful tool and has been shown to perform better than multiple linear regression. However neural-network PTFs are usually trained with an objective function that fits the estimated parameters of a soil hydraulic model. We called this the neuro-p method. The estimated parameters may carry errors and since the aim is to be able to estimate water retention, it is sensible to train the network to fit the measured water content. We propose a new objective function for neural network training, which predicts the parameters of the soil hydraulic model and optimizes the PTF to match the measured and observed water content, we called this neuro-m method. This method was used to predict the parameters of the van Genuchten model. Using Australian soil hydraulic data as a training set, neuro-m predicted the water retention from bulk density and particle-size distribution with a mean accuracy of 0.04 m3 m−3 The relative improvement of neuro-m over neural networks that was optimized to fit the parameters (neuro-p) is 13%. Compared with a published neural network PTF, the new method is 30% more accurate and less biased.

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Copyright © 2002. Soil Science SocietyPublished in Soil Sci. Soc. Am. J.66:352–361.