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Open AccessArticle

Equivalent Discharge Coefficient of Side Weirs in Circular Channel—A Lazy Machine Learning Approach

Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via G. Di Biasio 43, 03043 Cassino (FR), Italy
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Water 2019, 11(11), 2406; https://doi.org/10.3390/w11112406
Received: 22 August 2019 / Revised: 9 November 2019 / Accepted: 14 November 2019 / Published: 16 November 2019
(This article belongs to the Section Hydraulics)
Side weirs have been widely used since ancient times in many hydraulic works. Their operation can be analyzed following different approaches. However, almost all possible analysis approaches require knowledge of the discharge coefficient, which depends on several geometric and hydraulic parameters. An effective methodology for predicting discharge coefficient can be based on machine learning algorithms. In this research, experimental data obtained from tests carried out on a side weir in a circular channel and supercritical flow have been used to build predictive models of the equivalent discharge coefficient, by which the lateral outflow can be estimated by referring only to the flow depth upstream of the side weir. Four models, different in the input variables, have been developed. Each model has been proposed in 5 variants, depending on the applied algorithm. The focus is mainly on two lazy machine learning algorithms: k Nearest Neighbor and K-Star. The 5-input variables Model 1 and the 4-input variables Model 2 noticeably outperform the 3-input variables Model 3 and Model 4, showing that a suitable characterization of the side weir geometry is essential for a good accuracy of the prediction model. In addition, under models 1 and 2, k Nearest Neighbor and K-Star, despite the simpler structure, provide comparable or better performance than more complex algorithms such as Random Forest and Support Vector Regression. View Full-Text
Keywords: discharge coefficient; side weir; supercritical flow; machine learning algorithms; k Nearest Neighbor; K-Star discharge coefficient; side weir; supercritical flow; machine learning algorithms; k Nearest Neighbor; K-Star
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MDPI and ACS Style

Granata, F.; Di Nunno, F.; Gargano, R.; de Marinis, G. Equivalent Discharge Coefficient of Side Weirs in Circular Channel—A Lazy Machine Learning Approach. Water 2019, 11, 2406.

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