Development of Advanced Data-Intelligence Models for Radial Gate Discharge Coefficient Prediction: Modeling Different Flow Scenarios

Zaher Mundher Yaseen (), Omer A. Alawi (), Ammar Mohammed Alshammari (), Ali Alsuwaiyan (), Mojeed Opeyemi Oyedeji () and Atheer Y. Oudah ()
Additional contact information
Zaher Mundher Yaseen: King Fahd University of Petroleum & Minerals
Omer A. Alawi: Universiti Teknologi Malaysia
Ammar Mohammed Alshammari: King Fahd University of Petroleum & Minerals
Ali Alsuwaiyan: King Fahd University of Petroleum and Minerals
Mojeed Opeyemi Oyedeji: King Fahd University of Petroleum & Minerals (KFUPM)
Atheer Y. Oudah: University of Thi-Qar

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 14, No 14, 5677-5705

Abstract: Abstract This research aims to predict a radial gate's discharge coefficient (Cd) under free and submerged flow conditions using several machine learning (ML) algorithms. Several parameters are used to develop the learning process of the ML algorithms, including the gate radius (R), gate opening height (W), depth of water upstream (Yo), depth of water downstream (YT), trunnion pin height (h), and width (B). For this purpose, various new versions of ML models have been developed, such as the bagging regression tree (BGRT), bidirectional recurrent neural network (Bi-RNN), bidirectional long short-term memory (Bi-LSTM), Light Gradient Boosted Machine (LightGBM) Ensemble, Multiple Additive Regression Trees (MART), and Neural Regression Forests (NRFs). This study was extended to examine the sensitivity of the adopted predictors for Cd prediction. The adopted ML models generally achieved good and acceptable predictability. In quantitative metrics, Cd was accurately predicted using the Bi-LSTM model with a minimum value of mean absolute percentage error (MAPE = 2.245) and maximum Willmott index (WI = 0.861) over the testing phase for the free-flow condition. For the submerged flow condition, the BGRT model attained the best results, with (MAPE = 2.899) and (WI = 0.900).

Keywords: Machine learning models; Discharge coefficient; Radial gate; Free and submerged flow (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-023-03624-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:37:y:2023:i:14:d:10.1007_s11269-023-03624-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-023-03624-8

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().