Assessment of discharge coefficient in trapezoidal and rectangular canals through regularized extreme learning machine. (August 2021)
- Record Type:
- Journal Article
- Title:
- Assessment of discharge coefficient in trapezoidal and rectangular canals through regularized extreme learning machine. (August 2021)
- Main Title:
- Assessment of discharge coefficient in trapezoidal and rectangular canals through regularized extreme learning machine
- Authors:
- Chia Khani, Mohammad
Shabanlou, Saeid - Abstract:
- Highlights: Extreme learning machine (ELM). RELM. Coefficient of discharge (Cd). Partial derivative sensitivity analysis (PDSA). Abstract: Since side weirs efficiently serve in flood management plans, irrigation canals and drainage systems, the estimation or simulation of its discharge coefficient seems to be a necessary task. Moreover, as the determination of this coefficient is perhaps the most important factor for the design of a side weir, there are numerous studies focused on this regard. As for the first case, this paper aims at the utilization of the modern regularized extreme learning machine (RELM) method for simulating the discharge capacity of side weirs within trapezoidal and rectangular flumes. At the first step, the parameters affecting the discharge coefficient are detected, and then 19 RELM models are extended using them. To train and test the RELM models. 70% and 30% of the experimental data are employed, respectively. In addition, the hidden layer neurons and also the activation function belonging to the RELM model are optimized. In other words, the number of neurons is considered as 14 and the activation function is introduced as the best. The superior model predicts the target values using the Froude number, the slope of main conduit walls and ratio of the weir crest to upstream flow depth. The superior model of RELM is very powerful and even shows a better performance in comparison with the ELM model. For instance, the correlation coefficient, ScatterHighlights: Extreme learning machine (ELM). RELM. Coefficient of discharge (Cd). Partial derivative sensitivity analysis (PDSA). Abstract: Since side weirs efficiently serve in flood management plans, irrigation canals and drainage systems, the estimation or simulation of its discharge coefficient seems to be a necessary task. Moreover, as the determination of this coefficient is perhaps the most important factor for the design of a side weir, there are numerous studies focused on this regard. As for the first case, this paper aims at the utilization of the modern regularized extreme learning machine (RELM) method for simulating the discharge capacity of side weirs within trapezoidal and rectangular flumes. At the first step, the parameters affecting the discharge coefficient are detected, and then 19 RELM models are extended using them. To train and test the RELM models. 70% and 30% of the experimental data are employed, respectively. In addition, the hidden layer neurons and also the activation function belonging to the RELM model are optimized. In other words, the number of neurons is considered as 14 and the activation function is introduced as the best. The superior model predicts the target values using the Froude number, the slope of main conduit walls and ratio of the weir crest to upstream flow depth. The superior model of RELM is very powerful and even shows a better performance in comparison with the ELM model. For instance, the correlation coefficient, Scatter Index and Nash-Sutcliffe Efficiency Coefficient for the RELM superior model are estimated to be 0.982, 0.043 and 0.963. A formula is suggested for approximating the target function for application works. Lately, a partial derivative sensitivity analysis (PDSA) is executed for the provided equation. … (more)
- Is Part Of:
- Measurement. Volume 180(2021)
- Journal:
- Measurement
- Issue:
- Volume 180(2021)
- Issue Display:
- Volume 180, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 180
- Issue:
- 2021
- Issue Sort Value:
- 2021-0180-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-08
- Subjects:
- Discharge coefficient -- Trapezoidal and rectangular canals -- Regularized extreme learning machine -- Modeling
Weights and measures -- Periodicals
Measurement -- Periodicals
Measurement
Weights and measures
Periodicals
530.8 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02632241 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.measurement.2021.109493 ↗
- Languages:
- English
- ISSNs:
- 0263-2241
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5413.544700
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British Library HMNTS - ELD Digital store - Ingest File:
- 17204.xml