Prediction of Scour around Cross-Vane Structures using Generalized Structure of Group Method of Data Handling

Subject Areas : Farm water management with the aim of improving irrigation management indicators

ebrahim shahbazbigi ¹ ( Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran )
fariborz yosefvand ² ( Assistance Professor, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. )
behrouz yaghoubi ³ ( Assistance Professor, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. )
saeid shabanlou ⁴ ( Associate Professor, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran )
ahmad rajabi ⁵ ( Assistance Professor, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran )

Received: 2020-10-31 Accepted : 2021-05-16 Published : 2021-10-23

Keywords: Scour, Group Method of Data Handling, Uncertainty Analysis, Cross-Vane Structures, Partial derivative sensitivity analysis,

Abstract :

In this study, the scour pattern in the vicinity of cross-vane structures with I, U and J shapes in bending channels is simulated by a new artificial intelligence method called "generalized structures group method of data handling” (GSGMDH). Initially, all the parameters affecting the scour depth in the vicinity of cross-vane structures are identified and then using these parameters, six different models are defined for each of the GMDH and GSGMDH methods. By analyzing the results yielded by the artificial intelligence models, the superior models are introduced. The GMDH and GSGMDH superior models estimate the scour values in terms of all input parameters. In addition, the accuracy of the GSGMDH models is higher than that the GMDH ones. For example, for the GMDH and GSGMDH superior models, the values of "variance accounted for" in the test mode are calculated 73.075 and 86.408, respectively. Also, the superior model forecasts the objective function values with acceptable accuracy. For example, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliffe model efficiency coefficient (NSC) for the GSGMDH superior model in the training mode are approximated 0.913, 0.214 and 0.800, respectively. Based to the results of the sensitivity analysis, the shape factor of cross-vane structures, the ratio of the difference between the upstream and downstream flow depths to the height of the structure and the densimetric Froude number (Fd) are introduced as the most effective input parameters. An uncertainty analysis exhibits that the GSGMDH superior model has an underestimated performance.

References:

Azimi, H., Bonakdari, H. and Ebtehaj, I. 2019. Gene expression programming-based approach for predicting the roller length of a hydraulic jump on a rough bed. ISH Journal of Hydraulic Engineering, 1-11.
Azimi, H., Bonakdari, H., Ebtehaj, I., Gharabaghi, B. and Khoshbin, F. 2018. Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length. Acta Mechanica, 229(3): 1197-1214.
Azimi, H., Bonakdari, H., Ebtehaj, I., Shabanlou, S., Talesh, S.H.A. and Jamali, A. 2019. A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth. Sādhanā, 44(7): 160-169.
Azimi, H., Bonakdari, H., Ebtehaj, I., Talesh, S.H.A., Michelson, D.G. and Jamali, A. 2017. Evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition. Fuzzy Sets and Systems, 319: 50-69.
Ivakhnenko, A.G. 1976. The group method of data handling in prediction problems. Soviet Automatic Control, 9(6): 21-30.
Karbasi, M. and Azamathulla, H.M. 2016. GEP to predict characteristics of a hydraulic jump over a rough bed. KSCE Journal of Civil Engineering, 20(7): 3006-3011.
Khosronejad, A., Kozarek, J.L., Diplas, P., Hill, C., Jha, R., Chatanantavet, P., Heydari, N. and Sotiropoulos, F. 2018. Simulation-based optimization of in-stream structures design: rock vanes. Environmental Fluid Mechanics, 18(3): 695-738.
Kurdistani, S.M. and Pagliara, S. 2017. Experimental study on cross-vane scour morphology in curved horizontal channels. Journal of Irrigation and Drainage Engineering, 143(7): 04017013.
Kurdistani, S.M. and Pagliara, S. 2015. Scour characteristics downstream of grade-control structures: Log-vane and log-deflectors comparison. In World Environmental and Water Resources Congress, (1831-1840).
Najafzadeh, M. 2015. Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions. Ocean Engineering, 99: 85-94.
Pagliara, S., Hassanabadi, L. and Kurdistani, S.M. 2015b. Clear water scour downstream of log deflectors in horizontal channels. Journal of Irrigation and Drainage Engineering, 141(9): 04015007.
Pagliara, S. and Kurdistani, S.M. 2013. Scour downstream of cross-vane structures. Journal of hydroenvironment research, 7(4): 236-242.
Pagliara, S. and Kurdistani, S.M. 2015. Clear water scour at J-Hook Vanes in channel bends for stream restorations. Ecological engineering, 83: 386-393.
Pagliara, S. and Kurdistani, S.M. 2017. Flume experiments on scour downstream of wood stream restoration structures. Geomorphology, 279: 141-149.
Pagliara, S., Kurdistani, S.M. and Cammarata, L. 2013b. Scour of clear water rock W-weirs in straight rivers. Journal of Hydraulic Engineering, 140(4): 06014002.
Pagliara, S., Kurdistani, S. M., Palermo, M. and Simoni, D. 2016. Scour due to rock sills in straight and curved horizontal channels. Journal of hydro-environment research, 10: 12-20.
Pagliara, S., Kurdistani, S.M. and Santucci, I. 2013a. Scour downstream of J-Hook vanes in straight horizontal channels. Acta Geophysica, 61(5): 1211-1228.
Pagliara, S., Sagvand Hassanabadi, L. and Mahmoudi Kurdistani, S. 2015a. Log‐vane scour in clear water condition. River Research and Applications, 31(9): 1176-1182.
Shabanlou, S., Azimi, H., Ebtehaj, I. and Bonakdari, H. 2018. Determining the scour dimensions around submerged vanes in a 180 bend with the gene expression programming technique. Journal of Marine Science and Application, 17(2): 233-240.

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