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A Robust Evolutionary Design of Generalized Structure Group Method of Data Handling to Estimate Discharge Coefficient of Side Weir in Trapezoidal Channels

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

As one of the most applicable flow diversion structures, side weirs are utilized for adjusting and measuring the flow in the floodplain. In practice, in order to facilitate the design, the cross section of main channels is constructed trapezoidal. In this paper, a modern evolutionary artificial intelligence approach entitled "Generalized Structure Group Method of Data Handling (GSGMDH)" is used for the first time for approximating and modeling the discharge coefficient of side weirs placed upon trapezoidal main channels. Compared to the group method of data handling (GMDH) classical method, GSGMDH has more flexibility and higher ability in estimating different phenomena, because nodes existing in the hidden layer can make connection with non-adjacent layers. Initially, all parameters influencing the discharge coefficient of side weirs installed on trapezoidal canals are identified. Then, seven GSGMDH models with various architectures are developed using the mentioned parameters. For training the artificial intelligence models, 70% of the experimental data are implemented and the remaining 30% are used for testing them. The superior model is introduced through the analysis of the modeling results. The superior model simulates the discharge coefficient values with acceptable accuracy. For example, the values of the correlation coefficient (R), Scatter Index (SI) and the Nash–Sutcliffe efficiency coefficient (NSC) are calculated equal to 0.987, 0.025 and 0.987, respectively, in the testing mode. Based on the sensitivity analysis results, the flow Froude number (Fr) and the ratio of the height of the side weir crest to the flow depth at the weir upstream (W/y1) are introduced as the most influencing input parameters. Subsequently, the results of the superior GSGMDH model are compared with the classical GMDH model and it is revealed that the GSGMDH has a greater performance. For instance, about 19% of the GMDH model results have errors more than 10%, while this is about 2% for those of the GSGMDH. After that, an uncertainty analysis is carried out for these models to exhibit that the GSGMDH has an underestimated performance. Moreover, the comparison of the superior model results with the previous studies confirms the superiority of the GSGMDH over the earlier artificial intelligence models. Lastly, a formula is put forward for the superior GSGMDH model for evaluating the effects of the input parameters on the changing pattern of the objective parameter through the conduction of a partial derivative sensitivity analysis (PDSA).

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Authors and Affiliations

  1. Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    Mohammad Chia Khani & Saeid Shabanlou

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  1. Mohammad Chia Khani
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  2. Saeid Shabanlou
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Correspondence to Saeid Shabanlou.

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Khani, M.C., Shabanlou, S. A Robust Evolutionary Design of Generalized Structure Group Method of Data Handling to Estimate Discharge Coefficient of Side Weir in Trapezoidal Channels. Iran J Sci Technol Trans Civ Eng 46, 585–602 (2022). https://doi.org/10.1007/s40996-021-00594-y

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  • DOI: https://doi.org/10.1007/s40996-021-00594-y

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