Assessment of water surface profile in nonprismatic compound channels using machine learning techniques

Abstract

Accurate prediction of water surface profile in an open channel is the key to solving numerous critical engineering problems. The goal of the current research is to predict the water surface profile of a compound channel with converging floodplains using machine learning approaches, including gene expression programming (GEP), artificial neural networks (ANNs), and support vector machines (SVMs), in terms of both geometric and flow variables, as past studies were more focused on geometric variables. A novel equation was also proposed using gene expression programming to predict the water surface profile. In order to evaluate the performance and efficacy of these models, statistical indices are used to validate the produced models for the experimental analysis. The findings demonstrate that the suggested ANN model accurately predicted the water surface profile, with coefficient of determination (R2) of 0.999, root mean square error (RMSE) of 0.003, and mean absolute percentage error (MAPE) of 0.107%, respectively, when compared with GEP, SVM, and previously developed methods. The study confirms the application of machine learning approaches in the field of river hydraulics, and forecasting water surface profile of nonprismatic compound channels using a proposed novel equation by gene expression programming made this study unique. HIGHLIGHTS The present study predicted the water surface profile in nonprismatic compound channels with the help of various machine learning approaches.; The water surface profile is found to be affected by many nondimensional geometric and flow variables.; The findings depict that the water surface profile predicted using ANN is in good agreement with the observed water surface profile.;

Keywords

• geometric and flow parameters
• machine learning techniques
• nonprismatic compound channel
• statistical analysis
• water surface profile