Predicting and mapping the soil available water capacity of Australian wheatbelt

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@Article{Padarian:2014:GR,
  author =       "J. Padarian and B. Minasny and A. B. McBratney and 
                 N. Dalgliesh",
  title =        "Predicting and mapping the soil available water
                 capacity of Australian wheatbelt",
  journal =      "Geoderma Regional",
  volume =       "2-3",
  pages =        "110--118",
  year =         "2014",
  ISSN =         "2352-0094",
  DOI =          "doi:10.1016/j.geodrs.2014.09.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2352009414000133",
  abstract =     "Soil available water capacity (AWC) is the main source
                 of water for vegetation and it is the potential amount
                 of water available for atmospheric exchange. Studying
                 its spatial distribution is crucial for agricultural
                 planning and management and for use in biophysical
                 modelling. The aim of this work is to obtain a
                 continuous spatial prediction of AWC over Australia's
                 wheat belt (about 1.75 million km2), using digital soil
                 mapping techniques. We used a data set of 806 soil
                 profiles which have field measurements of drainage
                 upper limit (DUL) and crop lower limit (CLL). We mapped
                 AWC at five depth intervals (0-5, 5-15, 15-30, 30-60,
                 and 60-100 cm) with the help of different combinations
                 of environmental information (topographic, climatic,
                 soils, landsat imagery, gamma-ray spectrometry) as
                 covariates. The modelling techniques used were symbolic
                 regression (GP), Cubist, and support vector machines
                 (SVM). We also tried two averaging methods to generate
                 an ensemble model. We observed decreasing RMSE values
                 with the addition of extra covariates and also an
                 expected decreasing soil depth. In general, SVM
                 produced the best accuracy. We were able to improve the
                 predictions using one of the ensemble techniques, based
                 on a weighted average of GP, Cubist and SVM model. The
                 map generated with the optimal ensemble model was an
                 unrealistic representation of AWC therefore we decided
                 to present a sub-optimal model as the final map. We
                 stress the need to not only focus on the numerical
                 performance in order to obtain a flexible and stable
                 model, but also a coherent visual representation
                 without anomalies.",
  keywords =     "genetic algorithms, genetic programming, Ensemble
                 model, Field capacity",
}

Genetic Programming entries for Jose Padarian Budiman Minasny Alexander B McBratney Neal P Dalgliesh

Citations