Abstract
Slide gates are generally used to adjust the flow in open canals. The discharge coefficient (C-d) for a slide gate is a function of the geometric and hydraulic characteristics of the gate. For free flow conditions, C-d depends on the upstream water level and opening of the gate, while for submerged flow, it also depends on the downstream water level. The main aim of this study is to conduct a series of laboratory experiments to determine C-d for inclined slide gates and assess data-driven models for estimating C-d. These tests and models were used to evaluate both free and submerged flows. Experiments with inclination angles of 0 (a vertical slide gate), 15 degrees, 30 degrees and 45 degrees were studied with different gate openings. Results show that inclination of slide gates has a progressive effect on C-d and increases the flow capacity under the gate. The increase in C-d relates to the convergence of the flow through the gate opening. The Gradient Boosted Tree (GBT), Deep Learning (MLP-SGD) and Support Vector Machine (SVM) models as well as linear and non-linear regressions were evaluated for estimating C-d. The DL (MLP-SGD) model with Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE) of 0.913, 0.015, and 0.012, respectively, had optimal efficiency compared to the SVM and GBT models. In addition, a nonlinear regression equation, with NSE, RMSE and MAE values of 0.961, 0.012 and 0.007 performs better than AI models in free-flow conditions. The GBT model values of NSE, RMSE and MAE were calculated to be 0.954, 0.029 and 0.024, respectively. This model had a higher efficiency in submerged flow conditions.