Prediction of Manning Roughness Coefficient in Open Channels with Dune Bedforms Using Evolutionary Algorithm Method

An accurate prediction of the roughness coefficient in open channels with bedforms has a significant impact on the planning, design and operation of water resources projects, including water transport and river systems. Different bedforms such as dunes have obvious effects on flow resistance. However, due to the impact of various parameters on the roughness coefficient, accurate estimation of this parameter is difficult. In this paper, the efficiency of Gene Expression Programming (GEP) method in estimating manning roughness coefficient in open-channel channels with dune bedforms has been evaluated. In this regard, various models were defined based on flow, bedform, and sediment particles characteristics and were tested using four laboratory data series. The results proved capability of GEP in predicting Manning roughness coefficient and it was observed that the applied method is more accurate than semi-theoretical relationships. It was also found that the model with input parameters related to both flow and sediment particles characteristics is more successful in estimating Manning roughness coefficient. According to the results of the sensitivity analysis, the Reynolds number parameter has the most significant impact in predicting the roughness coefficient.