Furthermore, the above contention is substantiated by Figure 14, where the BPNN results are very near to the experimental values. Besides, outcomes of a single-factor ANOVA (Table 6) suggest that insignificant differences between observed and computed values have been found using all considered models. Therefore, the overall comparison of the outcomes suggests BPNN proved to be the most effective tool in computing the OTE of the Gabion weir. Reasons may be attributed to multiple flexibilities in tuning parameters like the number of hidden layers, the number of neurons in the hidden layer, momentum, learning rate, and epoch but BPNN has a lower number of tuning parameters in comparison to other proposed computing models, especially ML-based ANFIS models. So, optimal values of these tuning parameters can be achieved easily and hence give results closer to actual value (experimental value). Besides, the BPNN model has the capacity to compute and consider all complex and nonlinear variables which are responsible for oxygen transfer in the gabion weir flow, however other proposed models do not have such ability.