The current paper deals with the performance evaluation of the application of three soft computing algorithms such as adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural network (BPNN), and deep neural network (DNN) in predicting oxygen aeration efficiency (OAE20) of the gabion spillways. Besides, classical equations, namely multivariate linear and nonlinear regressions (MVLR and MVNLR), including previous studies, were also employed in predicting OAE20 of the gabion spillways. The analysis of results showed that the DNN demonstrated relatively lower error values (root mean square error, RMSE = 0.03465; mean square error, MSE = 0.00121; mean absolute error, MAE = 0.02721) and the highest value of correlation coefficient, CC = 0.9757, performed the best in predicting OAE20 of the gabion spillways; however, other applied models, such as ANFIS, BPNN, MVLR, and MVNLR, were giving comparable results evaluated to statistical appraisal metrics of the relative significance of input parameters based on sensitivity investigation, the porosity (n) of gabion materials was observed to be the most critical parameter, and gabion height (P) had the least impact over OAE20 of the spillways.

The current paper discussed the application and comparison of machine learning algorithms such as the gradient boosting machine (GBM), neural network (NN), and deep neural network (DNN) in estimating the oxygen aeration performance efficiency (OAPE20) of the gabion spillways. Besides, traditional equations, namely developed multivariable linear regression (MLR) and multivariable nonlinear regression (MNLR) along with the previous models were also employed in estimating OAPE20 of the gabion spillways. Results in the testing phase showed that the DNN with the highest value of correlation (correlation of coefficient (CC) = 0.9713) and lowest values of errors (root mean square error (RMSE) = 0.1684, mean squared error (MSE) = 0.0283, and mean absolute error (MAE) = 0.1532) demonstrated the best results in estimating OAPE20 of the gabion spillways; however, other applied models such as GBM, NN, MLR, and MNLR were giving comparable results evaluated to statistical appraisal metrics, but previous studies were performing incredibly poor with the lowest value of correlation and highest values of errors. The datasets employed here were collected by conducting experiments. From the relative significance of input parameters, the Reynolds number (Re) was observed to be a crucial parameter. At the same time, the ratio of the mean size gabion materials to the length of the gabion spillway (d50/L) had the least impact over the OAPE20 of the gabion spillways.