A key issue in the design of side weirs is the experimental assessment of the discharge coefficient. This can be determined by laboratory measurements of discharge and water depths at the up- and downstream ends of the weir by using De Marchi’s approach, consisting in the solution of the 1D dynamic equation of spatially varied steady flow with non-uniform discharge, under the assumption of energy conservation. This study originates from a recent alarming proliferation of works that evaluate the discharge coefficient for side weirs without clearly explaining the experimental methodology and/or even incorrectly applying modelling approaches, thus generating possible misinterpretations of the results. In this context, the present paper aims to highlight the effects of using oversimplified and/or heterogenous models (relying on different assumptions) for the experimental determination of the discharge coefficient for side weirs. Furthermore, a sensitivity analysis is performed to detect the most influencing hydraulic and geometric parameters on each considered model. The overall results clearly indicate the wrongness of using or building not homogeneous discharge coefficient datasets to obtain and/or compare predictive experimental discharge coefficient formulas. We finally show how neural networks could provide a possible solution to these heterogeneity issues.
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