Deep learning under H2O framework: A novel approach for quantitative analysis of discharge coefficient in sluice gates

Abstract

Gates in dams and irrigation canals have been used for the purpose of controlling discharge or water surface regulation. To compute the discharge under a gate, discharge coefficient (Cd) should be first determined precisely. From a novel point of view, this study investigates the effect of sill shape under the vertical sluice gate on Cd using four artificial intelligence methods, which are used to estimate Cd, (i) random forest (RF), (ii) deep learning (DL), (iii) gradient boosting machine (GBM), and (iv) generalized linear model (GLM). A sluice gate along with twelve different forms of sills was fabricated and tested in the University of Tabriz, Iran. Different flow rates were considered in the hydraulic laboratory with four gate openings. As a result, a total of 180 runs could be tested. The results showed that the installation of sill under the vertical gate has a positive effect on flow discharge. Sill shapes can be characterized by their hydraulic radius (Rs). Sensitivity analysis among the dimensionless parameters proved that Rs/G (the ratio of the hydraulic radius of the sills with respect to the gate opening) has a significant role in the determination of Cd. A semi-circular sill shape has a more positive effect on the increase of Cd than the other shapes.