A novel ensemble model for predicting the performance of a novel vertical slot fishway

doi:10.1007/s11709-020-0664-x

Front. Struct. Civ. Eng.

2020, Vol. 14

Issue (6) : 1418-1444 https://doi.org/10.1007/s11709-020-0664-x

RESEARCH ARTICLE

A novel ensemble model for predicting the performance of a novel vertical slot fishway

Aydin SHISHEGARAN¹(

), Mohammad SHOKROLLAHI², Ali MIRNOROLLAHI², Arshia SHISHEGARAN³, Mohammadreza MOHAMMAD KHANI⁴

¹. Department of Water and Environmental Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran
². Department of Water and Environmental Engineering, School of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
³. Department of Water and Environmental Engineering, School of Civil Engineering, Islamic Azad University Central Tehran Branch, Tehran 1987745815, Iran
⁴. School of Progress Engineering, Iran University of Science and Technology, Tehran 1684613114, Iran

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Abstract

We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models. Multiple linear regression, multiple log equation regression, gene expression programming, and combinations of these models are employed to predict the maximum turbulence, maximum velocity, resting area, and water depth of the middle pool in the fishway. The statistical parameters and error terms, including the coefficient of determination, root mean square error, normalized square error, maximum positive and negative errors, and mean absolute percentage error were employed to evaluate and compare the accuracy of the models. We also conducted a parametric study. The independent variables include the opening between baffles (OBB), the ratio of the length of the large and small baffles, the volume flow rate, and the angle of the large baffle. The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and OBB.

Keywords novel vertical slot fishway parametric study finite volume method ensemble model gene expression programming

Corresponding Author(s): Aydin SHISHEGARAN

Just Accepted Date: 13 November 2020 Online First Date: 30 December 2020 Issue Date: 12 January 2021

Cite this article:

Aydin SHISHEGARAN,Mohammad SHOKROLLAHI,Ali MIRNOROLLAHI, et al. A novel ensemble model for predicting the performance of a novel vertical slot fishway[J]. Front. Struct. Civ. Eng., 2020, 14(6): 1418-1444.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-020-0664-x
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I6/1418

Fig.1 A flowchart for explaining the process in the present study.

Fig.2 The considered boundary conditions for PFVSF.

mechanical and thermal property	symbol	unit	value
density	$ρ$	kg/m³	1000
temperature	T	°C	20
dynamic viscosity	μ	N·s/m²	0.001
kinematic viscosity	ν	m²/s	1 × 10⁻⁶

Tab.1 The properties of water at ambient temperature

Fig.3 The VSF geometry in the study of Ref. [¹⁴]. (a) The geometry and pool number of the VSF; (b) locations of velocity measurements in pool 5 and the dimensional geometry of all pools in the VSF.

Fig.4 The mesh sensitivity analysis for the first validation study [¹⁴].

Fig.5 Comparison of laboratory and numerical results of velocity. (a) The velocity in all directions and levels at point 1; (b) the velocity in all directions and levels at point 2 [¹⁴].

Fig.6 The obtained velocity distribution in the VSF from the FV model.

Fig.7 The geometry and dimension of the second VSF. (a) The geometry of the second VSF and the number of pools; (b) the location of the measured velocity in each pool (pools 1–4); (c) the dimensions of each pool of the second VSF in detail [⁴⁶].

Fig.8 The mesh sensitivity analysis for the second sample.

Fig.9 The comparison of the value of the reported velocity of the experiment and the obtained velocity from the FV model at 28 points [⁴⁶].

Fig.10 The obtained velocity distribution in pool 3 of the second VSF from the FV model.

Fig.11 The geometry of the PFVSF. (a) The dimensions and location of the small and large baffles in the PFVSF; (b) the geometry and dimensions of each pool in the PFVSF.

Tab.2 The variables of PFVSF in this study and their levels.

Fig.12 The maximum turbulence in pool 5 of the PFVSF. (a) Q = 1000 L/s and W₀ = 30 cm; (b) Q = 1000 L/s and W₀ = 45 cm; (c) Q = 1000 L/s and W₀ = 60 cm; (d) Q = 800 L/s and W₀ = 30 cm; (e) Q = 800 L/s and W₀ = 45 cm; (f) Q = 800 L/s and W₀ = 60 cm; (g) Q = 400 L/s and W₀ = 30 cm; (h) Q = 400 L/s and W₀ = 45 cm; (i) Q = 400 L/s and W₀ = 60 cm.

Fig.13 The maximum velocity in pool 5 of the PFVSF. (a) Q = 1000 L/s and W₀ = 30 cm; (b) Q = 1000 L/s and W₀ = 45 cm; (c) Q = 1000 L/s and W₀ = 60 cm; (d) Q = 800 L/s and W₀ = 30 cm; (e) Q = 800 L/s and W₀ = 45 cm; (f) Q = 800 L/s and W₀ = 60 cm; (g) Q = 400 L/s and W₀ = 30 cm; (h) Q = 400 L/s and W₀ = 45 cm; (i) Q = 400 L/s and W₀ = 60 cm.

Fig.14 The rest area in pool 5 of the PFVSF. (a) Q = 1000 L/s and W₀ = 30 cm; (b) Q = 1000 L/s and W₀ = 45 cm; (c) Q = 1000 L/s and W₀ = 60 cm; (d) Q = 800 L/s and W₀ = 30 cm; (e) Q = 800 L/s and W₀ = 45 cm; (f) Q = 800 L/s and W₀ = 60 cm; (g) Q = 400 L/s and W₀ = 30 cm; (h) Q = 400 L/s and W₀ = 45 cm; (i) Q = 400 L/s and W₀ = 60 cm.

Fig.15 The water depth in pool 5 of the PFVSF. (a) Q = 1000 L/s and W₀ = 30 cm; (b) Q = 1000 L/s and W₀ = 45 cm; (c) Q = 1000 L/s and W₀ = 60 cm; (d) Q = 800 L/s and W₀ = 30 cm; (e) Q = 800 L/s and W₀ = 45 cm; (f) Q = 800 L/s and W₀ = 60 cm; (g) Q = 400 L/s and W₀ = 30 cm; (h) Q = 400 L/s and W₀ = 45 cm; (i) Q = 400 L/s and W₀ = 60 cm.

Fig.16 The FP and velocity of the worst and best PFVSF at a depth of Z= 0.5H. (a) The FP in the worst PFVSF; (b) the FP in the best PFVSF; (c) the velocity in the worst PFVSF; (d) the velocity in the best PFVSF.

Fig.17 The values of each variable in 81 FV models. (a) The value of the volume flow rate in each sample; (b) OBB; (c) the length of the large baffle; (d) the length of the small baffle; (e) ALB.

Tab.3 The correlation coefficient between output and input variables

Fig.18 GEP flowchart.

Fig.19 A flowchart for the ensemble model.

Tab.4 The statistical parameters for evaluating the accuracy of the models in predicting the maximum turbulence

Tab.5 The error terms of each model for predicting the maximum turbulence in pool 5 of the PFVSF

Fig.20 Comparing the obtained results from FLOW-3D and GEP. (a) The error distribution for predicting the maximum turbulence; (b) the comparison of the obtained maximum turbulence from FLOW-3D and the predicted maximum turbulence by GEP.

Tab.6 The statistical parameters for evaluating the accuracy of the models in predicting the maximum velocity

Tab.7 The error terms of each model for predicting the maximum velocity in pool 5 of the PFVSF

Fig.21 Comparing the obtained results from FLOW-3D and the ensemble model. (a) The error distribution for predicting the maximum velocity; (b) the comparison of the obtained maximum velocity from FLOW-3D and the predicted maximum velocity by the ensemble model.

Tab.8 The statistical parameters and error terms of the models for predicting the rest area

Fig.22 Comparing the obtained results from FLOW-3D and the ensemble model. (a) The error distribution for predicting the rest area; (b) the comparison of the obtained rest area from FLOW-3D and the predicted rest area by the ensemble model.

Tab.9 The statistical parameters for evaluating the accuracy of the models in predicting the water depth

Tab.10 The error terms of each model for predicting the water depth in pool 5 of the PFVSF.

Fig.23 Comparing the obtained results from FLOW-3D and the ensemble model. (a) The error distribution for predicting the water depth; (b) the comparison of the obtained water depth from FLOW-3D and the predicted water depth by the ensemble model.

Tab.11 The results of AIC for all models in predicting each output

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