Next Article in Journal
Numerical Analysis of Recharge Rates and Contaminant Travel Time in Layered Unsaturated Soils
Next Article in Special Issue
Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model
Previous Article in Journal
Pollution and Sustainability Indices for Small and Medium Wastewater Treatment Plants in the Southwest of Spain
Previous Article in Special Issue
Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain
Article Menu
Issue 3 (March) cover image

Export Article

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)
  |  
PDF [4285 KB, uploaded 16 March 2019]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Salazar, F.; Crookston, B.M. A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways. Water 2019, 11, 544.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top