Estimating Soil Water Retention Curve by Extreme Learning Machine, Radial Basis Function, M5 Tree and Modified Group Method of Data Handling Approaches

This study was conducted to assess the applicability of novel types of neural network methods (extreme learning machine (ELM), radial basis function (RBF), modified group method of data handling (M-GMDH)) and M5 tree methods for the prediction of the soil water retention curve (SWRC) and compare their performance with that of derived methods and pedotransfer functions (PTFs) of other studies for soils in Iran. Then, 15 PTFs were developed. Predictions were evaluated by the integral root mean square error (IRMSE), integral mean error (IME), Akaike's information criterion (AIC), and coefficient of determination (R2). The RBF-based PTFs were better than the M5 tree, M-GMDH, and ELM-based PTFs in terms of the IRMSE criterion in the testing step. Also, PTFs and methods developed in the present study were more reliable than other derived PTFs and methods by different researchers. The values of the IRMSE and R2 in the best PTFs (with inputs of sand, clay, total porosity and the moisture content at field capacity and permanent wilting point) of the testing data set of the RBF method were 0.037 cm3 cm−3 and 0.988, respectively. For the testing data set, the average values of the IRMSE criterion for all the PTFs of the RBF, ELM, M-GMDH, and M5 tree methods were 0.051, 0.062, 0.055, and 0.054 cm3 cm−3, respectively. Therefore, the differences were considerable only between the ELM and other methods. The IRMSE criterion results of the testing data set showed the suitability of the RBF method in the development of PTFs for the prediction of the SWRC.