Modeling of the oxygen aeration performance efficiency of gabion spillways

Abstract

Abstract 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.