Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

  • Amir LAKZIAN Ferdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, Mashhad
  • Mohammad BANNAYAN AVAL Ferdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, Mashhad
  • Nasrin GORBANZADEH Ferdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, Mashhad
Keywords: pedotransfer functions, kNN approach, soil water content estimation, artificial neural network

Abstract

This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

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Author Biography

Mohammad BANNAYAN AVAL, Ferdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, Mashhad
Associate Professor
Published
2010-09-27
How to Cite
LAKZIAN, A., BANNAYAN AVAL, M., & GORBANZADEH, N. (2010). Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters. Notulae Scientia Biologicae, 2(3), 114-120. https://doi.org/10.15835/nsb234737
Section
Research articles

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