Statistical and Scaling Analyses of Neural Network Soil Property Inputs/Outputs at an Arizona Field Site

Alberto Guadagnini, Shlomo P. Neuman, Marcel G. Schaap, Monica Riva

2013

Abstract

Analyses of flow and transport in the shallow subsurface require information about spatial and statistical distributions of soil hydraulic properties (water content and permeability, their dependence on capillary pressure) as functions of scale and direction. Measuring these properties is relatively difficult, time consuming and costly. It is generally much easier, faster and less expensive to collect and describe the makeup of soil samples in terms of textural composition (e.g. per cent sand, silt, clay and organic matter), bulk density and other such pedological attributes. Over the last two decades soil scientists have developed a set of tools, known collectively as pedotransfer functions (PTFs), to help translate information about the spatial distribution of pedological indicators into corresponding information about soil hydraulic properties. One of the most successful PTFs is the nonlinear Rosetta neural network model developed by one of us. Among remaining open questions are the extents to which spatial and statistical distributions of Rosetta hydraulic property outputs, and their scaling behavior, reflect those of Rosetta pedological inputs. We address the last question by applying Rosetta, coupled with a novel statistical scaling analysis recently proposed by three of us, to soil sample data from an experimental site in southern Arizona, USA.

References

  1. Efron, B., Tibshirani, R., 1993. An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC.
  2. Guadagnini, A., Riva, M., Neuman, S.P., 2012. Extended power-law scaling of heavy-tailed random airpermeability fields in fractured and sedimentary rocks, Hydrol. Earth Syst. Sci., 16: 3249-3260, doi:10.5194/hess-16-3249-2012.
  3. Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12(3): 513-522.
  4. Neuman, S.P., Guadagnini, A., Riva, M., Siena, M., (2013). Recent advances in statistical and scaling analysis of earth and environmental variables, in Recent Advances in Hydrogeology, Springer, (invited), in press.
  5. Pachepsky, Y., Rawls, W.J. (Eds.), 2004. Development of Pedotransfer Functions in Soil Hydrology, Elsevier, Amsterdam, The Netherlands.
  6. Riva, M., Neuman, S.P., Guadagnini, A., 2013a. SubGaussian model of processes with heavy tailed distributions applied to permeabilities of fractured tuff, Stoch. Environ. Res. Risk Assess., 27: 195-207, doi:10.1007/s00477-012-0576-y.
  7. Riva, M., Neuman, S.P., Guadagnini, A., Siena, M., 2013b. Anisotropic scaling of Berea sandstone log air permeability statistics, Vadose Zone Jour., doi:10.2136/vzj2012.015,3in press.
  8. Schaap, M.G., 2013. Description, analysis and interpretation of an infiltration experiment in a semiarid deep vadose zone, in Recent Advances in Hydrogeology, Springer, (invited), in press.
  9. Schaap, M.G., Leij, F.J., 1998. Database related accuracy and uncertainty of pedotransfer functions, Soil Science, 163:765-779.
  10. Schaap, M.G., Leij, F.J., van Genuchten, M.Th., 2001. Rosetta: a Computer Program for Estimating Soil Hydraulic Parameters with Hierarchical Pedotransfer Functions, Journal of Hydrology, 251:163-176.
  11. Schaap, M.G., Nemes, A., Van Genuchten, M.Th., 2004. Comparison of models for indirect estimation of water retention and available water in surface soils, Vadose Zone Journal, 3:1455-1463.
  12. Siena, M., Guadagnini, A., Riva, M., Neuman, S.P., 2012. Extended power-law scaling of air permeabilities measured on a block of tuff, Hydrol. Earth Syst. Sci., 16: 29-42, doi:10.5194/hess-16-29-2012.
  13. Twarakavi, N.K.C., Šimunek, J., Schaap, M.G., 2009. Development of pedotransfer functions for estimation of soil hydraulic parameters using support vector machine, Soil Science Society of Am. J., 73(5):1443- 1452.
  14. van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J.. 44:892-898.
Download


Paper Citation


in Harvard Style

Guadagnini A., P. Neuman S., G. Schaap M. and Riva M. (2013). Statistical and Scaling Analyses of Neural Network Soil Property Inputs/Outputs at an Arizona Field Site . In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCEC, (SIMULTECH 2013) ISBN 978-989-8565-69-3, pages 489-494. DOI: 10.5220/0004600804890494


in Bibtex Style

@conference{msccec13,
author={Alberto Guadagnini and Shlomo P. Neuman and Marcel G. Schaap and Monica Riva},
title={Statistical and Scaling Analyses of Neural Network Soil Property Inputs/Outputs at an Arizona Field Site},
booktitle={Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCEC, (SIMULTECH 2013)},
year={2013},
pages={489-494},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004600804890494},
isbn={978-989-8565-69-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: MSCCEC, (SIMULTECH 2013)
TI - Statistical and Scaling Analyses of Neural Network Soil Property Inputs/Outputs at an Arizona Field Site
SN - 978-989-8565-69-3
AU - Guadagnini A.
AU - P. Neuman S.
AU - G. Schaap M.
AU - Riva M.
PY - 2013
SP - 489
EP - 494
DO - 10.5220/0004600804890494