Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

doi:10.1007/s11709-020-0634-3

Front. Struct. Civ. Eng.

2020, Vol. 14

Issue (5) : 1097-1109 https://doi.org/10.1007/s11709-020-0634-3

TRANSDISCIPLINARY INSIGHT

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Zohreh SHEIKH KHOZANI¹(

), Khabat KHOSRAVI², Mohammadamin TORABI³, Amir MOSAVI^4,⁵, Bahram REZAEI⁶, Timon RABCZUK¹

¹. Institute of Structural Mechanics, Bauhaus Universität-Weimar, Weimar D-99423, Germany
². Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University, Sari 48181, Iran
³. Department of Civil and Environmental Engineering, Idaho State University, Pocatello, ID 83209, USA
⁴. School of the Built Environment, Oxford Brookes University, Oxford OX30BP, UK
⁵. Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest 1034, Hungary
⁶. Department of Civil Engineering, Bu-Ali Sina University, Hamedan 65178, Iran

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Abstract

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

Keywords compound channel machine learning SKM model shear stress distribution data mining models

Corresponding Author(s): Zohreh SHEIKH KHOZANI

Online First Date: 09 September 2020 Issue Date: 16 November 2020

Cite this article:

Zohreh SHEIKH KHOZANI,Khabat KHOSRAVI,Mohammadamin TORABI, et al. Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1097-1109.

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http://journal.hep.com.cn/fsce/EN/10.1007/s11709-020-0634-3
http://journal.hep.com.cn/fsce/EN/Y2020/V14/I5/1097

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	Zohreh SHEIKH KHOZANI
	Khabat KHOSRAVI
	Mohammadamin TORABI
	Amir MOSAVI
	Bahram REZAEI
	Timon RABCZUK

Fig.1 General view of an experimental flume.

Fig.2 The cross-section of prismatic compound channels illustrating various floodplain widths.

Tab.1 The range of the main hydraulic parameters in the prismatic compound channel

Fig.3 The schematic diagram of M5 algorithm.

Fig.4 Architecture of the gating and expert networks.

Fig.5 Measured vs. predicted shear stress values in the compound channel: (a) as a scatterplot for AR model in testing stage; (b) whole dataset for AR model; (c) as a scatterplot for M5P model in testing stage; (d) whole dataset for M5P model; (e) as a scatterplot for KStar model in testing stage; (f) whole dataset for KStar model; (g) as a scatterplot for RC model in testing stage; (h) whole dataset for RC model; (i) as a scatterplot for RF model in testing stage; (j) whole dataset for RF model.

Tab.2 Statistical parameters in the comparison between the soft computing methods

Fig.6 The shear stress distribution prediction in the compound channel by RF, Shannon and SKM models for (a) OPC 100-30, (b) OPC 100-40, (c) OPC 200-35, (d) OPC 200-45, (e) OPC 300-30, (f) OPC 300-40, (g) OPC 400-40, and (h) OPC 400-50.

Tab.3 Statistical parameters in the comparison between the RF, Shannon and SKM models

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