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Open AccessArticle

A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways

1
International Centre for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
2
Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, UT 84321, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(3), 544; https://doi.org/10.3390/w11030544
Received: 29 January 2019 / Revised: 8 March 2019 / Accepted: 13 March 2019 / Published: 16 March 2019
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
Labyrinth weirs provide an economic option for flow control structures in a variety of applications, including as spillways at dams. The cycles of labyrinth weirs are typically placed in a linear configuration. However, numerous projects place labyrinth cycles along an arc to take advantage of reservoir conditions and dam alignment, and to reduce construction costs such as narrowing the spillway chute. Practitioners must optimize more than 10 geometric variables when developing a head–discharge relationship. This is typically done using the following tools: empirical relationships, numerical modeling, and physical modeling. This study applied a new tool, machine learning, to the analysis of the geometrically complex arced labyrinth weirs. In this work, both neural networks (NN) and random forests (RF) were employed to estimate the discharge coefficient for this specific type of weir with the results of physical modeling experiments used for training. Machine learning results are critiqued in terms of accuracy, robustness, interpolation, applicability, and new insights into the hydraulic performance of arced labyrinth weirs. Results demonstrate that NN and RF algorithms can be used as a unique expression for curve fitting, although neural networks outperformed random forest when interpolating among the tested geometries. View Full-Text
Keywords: arced labyrinth weir; spillway discharge capacity; machine learning; random forests model; neural networks arced labyrinth weir; spillway discharge capacity; machine learning; random forests model; neural networks
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MDPI and ACS Style

Salazar, F.; Crookston, B.M. A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways. Water 2019, 11, 544. https://doi.org/10.3390/w11030544

AMA Style

Salazar F, Crookston BM. A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways. Water. 2019; 11(3):544. https://doi.org/10.3390/w11030544

Chicago/Turabian Style

Salazar, Fernando; Crookston, Brian M. 2019. "A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways" Water 11, no. 3: 544. https://doi.org/10.3390/w11030544

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