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Estimation of hydraulic and geometric parameters of a gravel-bed river such as dimensions of bedforms is very difficult task, although they play a fundamental role in river engineering projects. One of the methods to get essential information regarding the bedform characteristics is to find the relations between the flow parameters and bedform dimensions. We conducted this field study in the Babolroud River in northern Iran to investigate the application of double averaged method in unspecific gravel bedforms to evaluate friction factor. Using data collected from several river reaches with total length of 356 m of a gravel-bed river, the relationship between bedform geometry (height and the length of bedforms) and flow parameters including shear velocity, transport stage parameter with friction factor is investigated. Different methods for estimating bedforms dimensions are examined to assess the ability of predicting bedform parameters (length and height) in a gravel-bed river. Using bedform parameters, the contribution of particle and form friction is estimated. Results confirm the application of the double averaged method and existing bedform parameters for unspecific bedforms. There exists a similar trend between aspect ratio and friction factor in gravel bedforms.

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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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