{"title":"Flow Discharge Determination in Straight Compound Channels Using ANNs","authors":"A. Zahiri, A. A. Dehghani","volume":34,"journal":"International Journal of Computer and Information Engineering","pagesStart":2331,"pagesEnd":2335,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9671","abstract":"Although many researchers have studied the flow\r\nhydraulics in compound channels, there are still many complicated problems in determination of their flow rating curves. Many different\r\nmethods have been presented for these channels but extending them\r\nfor all types of compound channels with different geometrical and\r\nhydraulic conditions is certainly difficult. In this study, by aid of nearly 400 laboratory and field data sets of geometry and flow rating\r\ncurves from 30 different straight compound sections and using artificial neural networks (ANNs), flow discharge in compound channels was estimated. 13 dimensionless input variables including relative depth, relative roughness, relative width, aspect ratio, bed\r\nslope, main channel side slopes, flood plains side slopes and berm\r\ninclination and one output variable (flow discharge), have been used\r\nin ANNs. Comparison of ANNs model and traditional method\r\n(divided channel method-DCM) shows high accuracy of ANNs model results. The results of Sensitivity analysis showed that the relative depth with 47.6 percent contribution, is the most effective input parameter for flow discharge prediction. Relative width and\r\nrelative roughness have 19.3 and 12.2 percent of importance, respectively. 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