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

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.