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An Improved Architecture of Group Method of Data Handling for Stability Evaluation of Cross-sectional Bank on Alluvial Threshold Channels

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Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 506))

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Abstract

The depth and surface of the water in the center of stable channels are two variables the majority of river engineers have been studying. As, the natural profile shape formed on stable banks is of great importance in designing threshold channels with gravel beds, in this study, extensive experiments are done to examine a channel’s geometric shape dimensions in the stable state. A novel method called the Improved Architecture of the Group Method of Data Handling (IAGMDH) is designed to overcome the main limitation of the classical GMDH model, including considering only 2nd order polynomial, considering only two inputs for each neuron as well as don’t use of neurons of the non-adjacent layers. The developed IAGMDH is applied to estimate bank profile specifications of stable channels. Accordingly, the flow discharge (Q) and transverse distance of points (x) located on stable banks from the center line are considered as input parameters and vertical boundary level (y) of points are considered as the output parameter. The performance of IAGMDH is compared and evaluated with seven previous models proposed by other researchers, a well-known scheme of the GMDH that is optimized with the genetic algorithm (GMDH-GA) and a Non-Linear Regression (NLR) model. Comparing the nine models’ results with experimental data shows that the IAGMDH model outperformed (MARE = 0.5107, RMSE = 0.052, and R = 0.9848) others in testing mode and is thus more accurate than the other models. Vigilar and Diplas Model (VDM) with RMSE of 0.2934 performs better among previous relationships. The GMDH model presented in this study is similar to VDM, suggesting a polynomial curve shape for the proposed threshold’s cross-section. Among other shapes proposed, the polynomial curve is the most appropriate compared with experimental values. The IAGMDH model also offers a robust and straightforward relationship that can predict a variety of channels’ given cross-section dimensions; hence, the proposed approach can be employed in the design, construction, and operation of artificial channels and rivers.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Ottawa, 161 Louis Pasteur Drive, Ottawa, K1N 6N5, Canada

    Hossein Bonakdari

  2. Environmental Research Center, Razi University, Kermanshah, Iran

    Azadeh Gholami

  3. Department of Soils and Agri-Food Engineering, Université Laval, Quebec City, QC, G1V06, Canada

    Isa Ebtehaj

  4. School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Bahram Gharebaghi

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  1. Hossein Bonakdari
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  2. Azadeh Gholami
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  3. Isa Ebtehaj
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  4. Bahram Gharebaghi
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Correspondence to Hossein Bonakdari .

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  1. Saga University, Saga, Japan

    Kohei Arai

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Bonakdari, H., Gholami, A., Ebtehaj, I., Gharebaghi, B. (2022). An Improved Architecture of Group Method of Data Handling for Stability Evaluation of Cross-sectional Bank on Alluvial Threshold Channels. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_53

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