Estimating critical depth and discharge over sloping rough end depth using machine learning

Abstract

This study uses machine learning (ML) to predict the end-depth structure's discharge and critical depth (yc). Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used in this investigation. The findings indicate that the radial kernel function-based GP model is most suitable compared to other applied models with the lowest root-mean-square error = 0.0021, 0.007, normalized root-mean-square error = 0.0361, 0.0516 representing mean absolute error = 0.0015, 0.004 and the highest coefficient of correlation = 0.9912, 0.9916, Legates and McCabe's index = 0.8839, 0.9026 Willmott's index = 0.9956, 0.9956, and Nash Sutcliffe model efficiency = 0.9823, 09830 for yc for the end-depth structure (yc) and discharge (Q) with the testing stage, respectively. Results of the sensitivity study indicate that the friction coefficient is the most significant input variable compared to other parameters for predicting (yc) and flow running via the thickness model's last stage (Q) using this dataset. HIGHLIGHTS The abrupt reduction in the channel bed level is referred to as free overfall.; Free overfall is used to estimate the discharge flowing via open channels.; The discharge and critical depth for end depth structures are predicted using machine learning (ML).; Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used.; Radial kernel function-based GP model (GP_RBF) is the most suitable as compared to other applied models.;

Keywords

• bed roughness
• brink depth
• broad-crested weir
• critical depth
• end-depth
• flow discharge
• free over-fall
• machine learning