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Data-intelligence approaches for comprehensive assessment of discharge coefficient prediction in cylindrical weirs : Insights from extensive experimental data sets

Roushangar, Kiyoumars ; Shahnazi, Saman LU and Mehrizad, Amir (2024) In Measurement: Journal of the International Measurement Confederation 233.
Abstract

The present study collected a wide range of experimental data samples, including 855 records from various types of cylindrical weirs under diverse hydraulic conditions, setting the stage for an in-depth analysis for modeling of discharge coefficient. For this purpose, the prediction capability of four kernel-based methods (SVM, GPR, KELM, and KRR) and four neural network-based methods (FFBP, CFBP, GRNN, and RBFN) was thoroughly investigated. Results reveal that among the neural network methods, FFBP demonstrates superior performance with a correlation coefficient (R) of 0.973, Nash-Sutcliffe Efficiency (NSE) of 0.942, and Mean Squared Error (RMSE) of 0.014, particularly excelling at high flow rates. Conversely, KRR emerges as the... (More)

The present study collected a wide range of experimental data samples, including 855 records from various types of cylindrical weirs under diverse hydraulic conditions, setting the stage for an in-depth analysis for modeling of discharge coefficient. For this purpose, the prediction capability of four kernel-based methods (SVM, GPR, KELM, and KRR) and four neural network-based methods (FFBP, CFBP, GRNN, and RBFN) was thoroughly investigated. Results reveal that among the neural network methods, FFBP demonstrates superior performance with a correlation coefficient (R) of 0.973, Nash-Sutcliffe Efficiency (NSE) of 0.942, and Mean Squared Error (RMSE) of 0.014, particularly excelling at high flow rates. Conversely, KRR emerges as the top-performing kernel-based method with R = 0.901, NSE = 0.811, and RMSE = 0.058. Moreover, the prediction process unveils challenges associated with hydraulic conditions, notably the presence of upstream ramps, which substantially diminish the accuracy of modeling results by 112 %.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural networks, Cylindrical weir, Discharge coefficient, Discharge prediction, Kernel-based models
in
Measurement: Journal of the International Measurement Confederation
volume
233
article number
114673
publisher
Elsevier
external identifiers
  • scopus:85191334306
ISSN
0263-2241
DOI
10.1016/j.measurement.2024.114673
language
English
LU publication?
yes
id
278aac1a-8745-45f4-88e3-e10064ef5c37
date added to LUP
2024-05-07 15:24:01
date last changed
2024-05-07 15:24:11
@article{278aac1a-8745-45f4-88e3-e10064ef5c37,
  abstract     = {{<p>The present study collected a wide range of experimental data samples, including 855 records from various types of cylindrical weirs under diverse hydraulic conditions, setting the stage for an in-depth analysis for modeling of discharge coefficient. For this purpose, the prediction capability of four kernel-based methods (SVM, GPR, KELM, and KRR) and four neural network-based methods (FFBP, CFBP, GRNN, and RBFN) was thoroughly investigated. Results reveal that among the neural network methods, FFBP demonstrates superior performance with a correlation coefficient (R) of 0.973, Nash-Sutcliffe Efficiency (NSE) of 0.942, and Mean Squared Error (RMSE) of 0.014, particularly excelling at high flow rates. Conversely, KRR emerges as the top-performing kernel-based method with R = 0.901, NSE = 0.811, and RMSE = 0.058. Moreover, the prediction process unveils challenges associated with hydraulic conditions, notably the presence of upstream ramps, which substantially diminish the accuracy of modeling results by 112 %.</p>}},
  author       = {{Roushangar, Kiyoumars and Shahnazi, Saman and Mehrizad, Amir}},
  issn         = {{0263-2241}},
  keywords     = {{Artificial neural networks; Cylindrical weir; Discharge coefficient; Discharge prediction; Kernel-based models}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Measurement: Journal of the International Measurement Confederation}},
  title        = {{Data-intelligence approaches for comprehensive assessment of discharge coefficient prediction in cylindrical weirs : Insights from extensive experimental data sets}},
  url          = {{http://dx.doi.org/10.1016/j.measurement.2024.114673}},
  doi          = {{10.1016/j.measurement.2024.114673}},
  volume       = {{233}},
  year         = {{2024}},
}