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Soil Science Society of America Journal Abstract - Soil Physics

Use of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties


This article in SSSAJ

  1. Vol. 70 No. 2, p. 327-336
    Received: Apr 21, 2005

    * Corresponding author(s): anemes@hydrolab.arsusda.gov
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  1. Attila Nemes *ab,
  2. Walter J. Rawlsb and
  3. Yakov A. Pachepskyc
  1. a Dep. of Environmental Sciences, Univ. of California, Riverside, CA 92521
    b USDA-ARS Hydrology and Remote Sensing Lab., 10300 Baltimore Ave., Bldg. 007, BARC-West, Beltsville, MD 20705
    c USDA-ARS Environmental Microbial Safety Lab., Powder Mill Road, Bldg. 173, BARC-East, Beltsville, MD 20705


Nonparametric approaches are being used in various fields to address classification type problems, as well as to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm has been applied to estimate water retention at −33- and −1500-kPa matric potentials. Performance of the algorithm has subsequently been tested against estimations made by a neural network (NNet) model, developed using the same data and input soil attributes. We used a hierarchical set of inputs using soil texture, bulk density (Db), and organic matter (OM) content to avoid possible bias toward one set of inputs, and varied the size of the data set used to develop the NNet models and to run the k-NN estimation algorithms. Different ‘design-parameter’ settings, analogous to model parameters have been optimized. The k-NN technique showed little sensitivity to potential suboptimal settings in terms of how many nearest soils were selected and how those were weighed while formulating the output of the algorithm, as long as extremes were avoided. The optimal settings were, however, dependent on the size of the development/reference data set. The nonparametric k-NN technique performed mostly equally well with the NNet models, in terms of root-mean-squared residuals (RMSRs) and mean residuals (MRs). Gradual reduction of the data set size from 1600 to 100 resulted in only a slight loss of accuracy for both the k-NN and NNet approaches. The k-NN technique is a competitive alternative to other techniques to develop pedotransfer functions (PTFs), especially since redevelopment of PTFs is not necessarily needed as new data become available.

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