research-article

Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient

Authors:

Amir Hossein Zaji,

Hossein Bonakdari,

Saeed Reza Khodashenas,

Shahaboddin ShamshirbandAuthors Info & Claims

Applied Mathematics and Computation, Volume 274, Issue C

Pages 14 - 19

https://doi.org/10.1016/j.amc.2015.10.070

Published: 01 February 2016 Publication History

Abstract

A principal step in designing dividing hydraulic structures entails determining the side weir discharge coefficient. In this study, Firefly optimization-based Support Vector Regression (SVR-FF) is introduced and examined in terms of predicting the discharge coefficient of a modified labyrinth side weir. Ten non-dimensional parameters of various geometrical and hydraulic conditions are defined as the input parameters for the SVR-FF and the side weir discharge coefficient is defined as the output. Improvements in SVR prediction accuracy are determined by comparing SVR-FF with the traditional SVR model. The results indicate that the SVR-FF model with RMSE of 0.035 is about 10% more accurate than SVR with RMSE of 0.039. Thus, combining the Firefly optimization algorithm with SVR increases the prediction model performance.

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Cited By

Rahmanshahi MJafari-Asl JFathi-Moghadam MOhadi SMirjalili S(2024)Metaheuristic learning algorithms for accurate prediction of hydraulic performance of porous embankment weirsApplied Soft Computing10.1016/j.asoc.2023.111150151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111150
Mahmoud AHu TZeng XJing PLi XDa Costa Ribeiro E(2023)Hydraulic informed multi-layer perceptron for estimating discharge coefficient of labyrinth weirEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106435123:PCOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106435
Khosravi KSafari MSheikh Khozani ZCrookston BGolkarian A(2022)Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07073-026:22(12271-12290)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07073-0
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Published In

cover image Applied Mathematics and Computation

Applied Mathematics and Computation Volume 274, Issue C

February 2016

800 pages

ISSN:0096-3003

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 February 2016

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Cited By

Rahmanshahi MJafari-Asl JFathi-Moghadam MOhadi SMirjalili S(2024)Metaheuristic learning algorithms for accurate prediction of hydraulic performance of porous embankment weirsApplied Soft Computing10.1016/j.asoc.2023.111150151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111150
Mahmoud AHu TZeng XJing PLi XDa Costa Ribeiro E(2023)Hydraulic informed multi-layer perceptron for estimating discharge coefficient of labyrinth weirEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106435123:PCOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106435
Khosravi KSafari MSheikh Khozani ZCrookston BGolkarian A(2022)Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07073-026:22(12271-12290)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07073-0
Emami HEmami SParsa J(2022)A Walnut optimization algorithm applied to discharge coefficient prediction on labyrinth weirsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07041-826:22(12197-12215)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00500-022-07041-8

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