Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir
Broad-crested weirs are structures used to measure and control the water flows in rivers, canals, and irrigation and drainage networks. Accurate estimation of spillway discharge is one of the most striking elements in measurement structures. So far, many researchers have studied this issue based on...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | Article |
Published: |
HARD
2022
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/46298/ https://doi.org/10.15244/pjoes/147592 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.46298 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.462982024-07-22T08:27:57Z http://eprints.um.edu.my/46298/ Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir Safari, Samira Takarli, Atefeh Salarian, Mohammad Banejad, Hossein Heydari, Mohammad Ghadim, Hamed Benisi TC Hydraulic engineering. Ocean engineering Broad-crested weirs are structures used to measure and control the water flows in rivers, canals, and irrigation and drainage networks. Accurate estimation of spillway discharge is one of the most striking elements in measurement structures. So far, many researchers have studied this issue based on various experimental conditions and a specific range of optional variables. They also have presented several relations. In the present study, 113 data sets of Bos were used for applicability of Artificial Neural Network (ANN), Gene expression programming (GEP), regression models to estimate the discharge coefficient for the rectangular broad-crested weirs. The effectiveness of the models was calculated using statistical criteria, including the coefficient of determination (R2), Root Mean Square Error (RMSE), and mean absolute error ( MAE). Comparing the models showed that the ANN with the highest R-2 coefficient (0.9916), lowest RMSE = 0.0012, and MAE = 0.00052 has the best discharge coefficient estimation than GEP models, regression models, and other empirical relations for the rectangular broadcrested weirs. HARD 2022 Article PeerReviewed Safari, Samira and Takarli, Atefeh and Salarian, Mohammad and Banejad, Hossein and Heydari, Mohammad and Ghadim, Hamed Benisi (2022) Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir. POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 31 (5). pp. 4817-4827. ISSN 2083-5906, DOI https://doi.org/10.15244/pjoes/147592 <https://doi.org/10.15244/pjoes/147592>. https://doi.org/10.15244/pjoes/147592 10.15244/pjoes/147592 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
TC Hydraulic engineering. Ocean engineering |
spellingShingle |
TC Hydraulic engineering. Ocean engineering Safari, Samira Takarli, Atefeh Salarian, Mohammad Banejad, Hossein Heydari, Mohammad Ghadim, Hamed Benisi Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
description |
Broad-crested weirs are structures used to measure and control the water flows in rivers, canals, and irrigation and drainage networks. Accurate estimation of spillway discharge is one of the most striking elements in measurement structures. So far, many researchers have studied this issue based on various experimental conditions and a specific range of optional variables. They also have presented several relations. In the present study, 113 data sets of Bos were used for applicability of Artificial Neural Network (ANN), Gene expression programming (GEP), regression models to estimate the discharge coefficient for the rectangular broad-crested weirs. The effectiveness of the models was calculated using statistical criteria, including the coefficient of determination (R2), Root Mean Square Error (RMSE), and mean absolute error ( MAE). Comparing the models showed that the ANN with the highest R-2 coefficient (0.9916), lowest RMSE = 0.0012, and MAE = 0.00052 has the best discharge coefficient estimation than GEP models, regression models, and other empirical relations for the rectangular broadcrested weirs. |
format |
Article |
author |
Safari, Samira Takarli, Atefeh Salarian, Mohammad Banejad, Hossein Heydari, Mohammad Ghadim, Hamed Benisi |
author_facet |
Safari, Samira Takarli, Atefeh Salarian, Mohammad Banejad, Hossein Heydari, Mohammad Ghadim, Hamed Benisi |
author_sort |
Safari, Samira |
title |
Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
title_short |
Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
title_full |
Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
title_fullStr |
Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
title_full_unstemmed |
Evaluation of ANN, GEP, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
title_sort |
evaluation of ann, gep, and regression models to estimate the discharge coefficient for the rectangular broad-crested weir |
publisher |
HARD |
publishDate |
2022 |
url |
http://eprints.um.edu.my/46298/ https://doi.org/10.15244/pjoes/147592 |
_version_ |
1806442665293643776 |
score |
13.209306 |