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Estimating soil hydraulic conductivity using different data-driven models of ANN, GMDH and GMDH-HS

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Abstract

The saturated hydraulic conductivity (\(K_{\text{s}}\)) is one of the important soil hydraulic properties which plays a significant role in developing flow transport models and irrigation and drainage practices. In this research, artificial neural networks approaches, group method of data handling (GMDH) model and a hybrid intelligent model based on combination of GMDH and harmony search (HS) model (GMDH-HS) were developed to estimate \(K_{\text{s}}\) based on 151 field samples collected from the northeast of Iran. Eleven topsoil properties were used as input parameters to estimate \(K_{\text{s}}\). The five quantitative standard statistical performance evaluation measures, i.e., coefficient of efficiency, root-mean-square error, mean square relative error, mean absolute percentage error and relative bias, were employed to evaluate the performance of various developed models. Statistical results indicated that the best performance can be obtained by GMDH-HS in terms of different evaluated criteria during the training and testing datasets for \(K_{\text{s}}\) estimation.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Kourosh Qaderi

  2. Department of Soil Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Vahidreza Jalali & Samaneh Etminan

  3. Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mojtaba Masoumi Shahr-babak

  4. Department of Irrigation and Drainage, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

    Mehdi Homaee

Authors
  1. Kourosh Qaderi
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  2. Vahidreza Jalali
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  3. Samaneh Etminan
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  4. Mojtaba Masoumi Shahr-babak
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  5. Mehdi Homaee
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Correspondence to Vahidreza Jalali.

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Qaderi, K., Jalali, V., Etminan, S. et al. Estimating soil hydraulic conductivity using different data-driven models of ANN, GMDH and GMDH-HS. Paddy Water Environ 16, 823–833 (2018). https://doi.org/10.1007/s10333-018-0672-9

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  • DOI: https://doi.org/10.1007/s10333-018-0672-9

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