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In this study, the potential of soft computing techniques, namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH), was evaluated to predict the aeration efficiency (AE20) of Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models i.e., with multiple linear regression (MLR) and multiple nonlinear regression (MNLR). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting AE20 of Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods, respectively. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 of Parshall and Modified Venturi flumes.

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models