Dimensions of river bedforms have an effect on total roughness. The complexity of bedform development causes empirical methods to differentiate from each other in predicting bedform dimensions. In this paper, two novel hybrid intelligence models based on a combination of the group method of data handling (GMDH) with the harmony search (HS) algorithm and shuffled complex evolution (SCE) have been developed for predicting bedform dimensions. A data set of 446 field and laboratory measurements were used to evaluate the ability of the developed models. The results were compared to conventional GMDH models with two kinds of transfer functions and an empirical formula. Also, five different combinations of dimensionless parameters as input variables were examined for predicting bedform dimensions. Results reveal that GMDH-HS and GMDH-SCE have good performance in predicting bedform dimensions, and all artificial intelligence methods were dramatically different to the empirical formula of van Rijn showing that using these methods is a key to solving complexity in predicting bedform dimensions. Also, comparing different combinations of dimensionless parameters reveals that there is no significant difference between the accuracy of each combination in predicting bedform dimensions.

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