Oxygen aeration efficiency of gabion spillway by soft computing models

Abstract

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. HIGHLIGHTS An experimental study of the aeration performance of gabion spillways was studied.; Soft computing techniques have been used to evaluate the aeration performance of the gabion spillways using an experimental dataset.; DNN was found to be outperforming the model; however, the proposed ANFIS and BPNN models were performing well.; The sensitivity study suggested that the input parameter, i.e. porosity, was the most sensitive parameter.;

Keywords

• adaptive neuro-fuzzy inference systems (anfis)
• backpropagation neural network (bpnn)
• deep neural network (dnn)
• dissolved oxygen (do)
• gabion spillway
• oxygen aeration efficiency (oae20)