The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms

An accurate prediction of roughness coefficient is of substantial importance for river management. The current study applies two artificial intelligence methods namely; Feed-Forward Neural Network (FFNN) and Multilayer Perceptron Firefly Algorithm (MLP-FFA) to predict the Manning roughness coefficient in channels with dune and ripple bedforms. In this regard, based on the flow and sediment particles properties various models were developed and tested using some available experimental data sets. The obtained results showed that the applied methods had high efficiency in the Manning coefficient modeling. It was found that both flow and sediment properties were effective in modeling process. Sensitivity analysis proved that the Reynolds number plays a key role in the modeling of channel resistance with dune bedform and Froude number and the ratio of the hydraulic radius to the median grain diameter play key roles in the modeling of channel resistance with ripple bedform. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was performed and obtained results showed an allowable degree of uncertainty for the MLP-FFA model in roughness coefficient modeling. HIGHLIGHTS FFNN and MLP-FFA methods were selected to identify influential parameters for prediction of roughness coefficient in alluvial channel.; Experimental datasets were used to feed the utilized models.; Uncertainty analysis was performed to evaluate the best-applied model dependability.; Results showed desirable performance of the applied models in roughness coefficient modeling.