Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels

Most natural streams or rivers exhibit a compound or two-stage geometry consisting of a main channel and one or two floodplains. The discharge capacity of compound channels has an importance in flood defence schemes and in the economic development of floodplain areas for agriculture and parks. Therefore, the comprehensive stage–discharge model studies performed and different one or two-dimensional methods have been developed. In this study, the single-channel method (SCM), the divided-channel method (DCM), the coherence method (COHM), the exchange discharge method (EDM) and the Shiono–Knight method (SKM) have been compared with a multilayer perception neural network (MLP) with Levenberg–Marquardt algorithm. The results of the comparisons reveal that the artificial neural network (ANN) model gives slightly better statistical results than those of the COHM, EDM and these three give more accurate results than those of the SCM, DCM, and SKM.