Abstract
Channel flow–vegetation interaction has been extensively studied in the past few decades and many equations have been developed which essentially differ from each other in derivation and form. As the process is extremely complex, getting deterministic or analytical forms of process phenomena are too difficult. A hybrid neural network model (combining genetic algorithm with neural network), which is particularly useful in modeling processes about which adequate knowledge of the physics is limited, is presented here as a complementary tool to model channel flow–vegetation interactions in submerged vegetation conditions. The prediction capability of the model has been found to be satisfactory. The input significance of the different parameters has been analyzed in the present work in order to find out the influence of these parameters on channel flow velocity.
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