Approximation of aeration efficiency at sharp-crested weirs using metaheuristic regression approaches

Abstract

This paper explores the ability of multivariate adaptive regression splines, decision trees, Gaussian processes, and multiple non-linear regression equation approaches to predict the aeration efficiency at various weirs and discusses their results. In total, 126 experimental observations were collected in the laboratory, of which 88 were arbitrarily selected for model training, and the rest were used for model validation. Various graphical presentations and goodness-of-fit parameters were used to assess the performance of the models. Performance evaluation results, Whisker plot, and Taylor's diagram indicated that the GP_rbf-based model was superior to other implemented models in predicting the aeration efficiency of weirs with CC (0.9961 and 0.9973), MAE (0.0079 and 0.0195), RMSE (0.0122 and 0.0251), scattering index (0.0594 and 0.1238), and Nash Sutcliffe model efficiency (0.9923 and 0.9564) values in the training and validating stages, respectively. The predicted values by GP_rbf lie within the ±30% error line in the training and validating stages, with most of it lying at/close to the line of agreement. The random forest model had better predictability than other decision tree models implied. The sensitivity analysis of parameters suggests shape factor and drop height as major influencing factors in predicting the aeration efficiency. HIGHLIGHTS Experimental study to evaluate aeration efficiency at various shapes of sharp-crested weir models.; Application of machine learning techniques to predict aeration efficiency of sharp-crested weirs.; Introduction of shape factor for different shapes of weirs as an input to ML models.; Use of graphs and goodness-of-fit parameters to assess the performance of applied ML models.; Sensitivity analysis.;

Keywords

• aeration efficiency
• decision tree
• gaussian process
• multivariate adaptive regression splines
• non-linear regression equation
• weirs