Investigating the capability of two hybrid intelligence methods to predict bedform dimensions of alluvial channels

Dimensions of river bedforms have a sensible effect on total roughness. Complication of bedform development causes a differentiation of the empirical methods from each other in predicting bedform dimensions. In this paper, two novel hybrid intelligence models based on combination of Group Method of Data Handling (GMDH) with 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 developed models. The results were compared to conventional GMDH models with two kinds of transfer functions and empirical formula of van Rijn (1984). 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 have a dramatic difference with the empirical formula of van Rijn showing that using these methods is a key of 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.