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Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study

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  • Bonakdari, Hossein
  • Khozani, Zohreh Sheikh
  • Zaji, Amir Hossein
  • Asadpour, Navid

Abstract

Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel is used to quantify the momentum transfer between these sub-areas of a cross section. In order to simulate the apparent shear stress, two soft computing techniques, including the Genetic Algorithm-Artificial Neural Network (GA-ANN) and Genetic Programming (GP) along with Multiple Linear Regression (MLR) were used. The proposed GA-ANN is a novel self-hidden layer neuron adjustable hybrid method made by combining the Genetic Algorithm (GA) with the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) method. In order to find the optimum condition of the methods considered in modeling apparent shear stress, various input combinations, fitness functions, transfer functions (for the GAA method), and mathematical functions (for the GP method) were investigated. Finally, the results of the optimum GAA and GP methods were compared with the MLR as a basic method. The results show that the hybrid GAA method with RMSE of 0.5326 outperformed the GP method with RMSE of 0.6651. In addition, the results indicate that both GAA and GP methods performed significantly better than MLR with RMSE of 1.5409 in simulating apparent shear stress in symmetric compound channels.

Suggested Citation

  • Bonakdari, Hossein & Khozani, Zohreh Sheikh & Zaji, Amir Hossein & Asadpour, Navid, 2018. "Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 400-411.
    • Handle: RePEc:eee:apmaco:v:338:y:2018:i:c:p:400-411
    DOI: 10.1016/j.amc.2018.06.016
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    References listed on IDEAS

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    1. Zaji, Amir Hossein & Bonakdari, Hossein & Khodashenas, Saeed Reza & Shamshirband, Shahaboddin, 2016. "Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 14-19.
    2. Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
    3. Mehdi Rezaeian Zadeh & Seifollah Amin & Davar Khalili & Vijay Singh, 2010. "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2673-2688, September.
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