Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (C_d) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the C_d was determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the C_d. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Y _o) was the influential variable for the C_d under the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Y _o:Y_T) was the influential variable for the C_d under the submerged flow condition.