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Open AccessArticle

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

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Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
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Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
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Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
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Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
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Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Agricultural Department, Payam Noor University, Tehran, Iran
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Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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National Water Center, United Arab Emirates University, Al Ain, UAE
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Authors to whom correspondence should be addressed.
Water 2020, 12(3), 902; https://doi.org/10.3390/w12030902
Received: 27 November 2019 / Revised: 17 February 2020 / Accepted: 4 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions. View Full-Text
Keywords: scour rate; prediction model; optimization model; MLP model; colliding bodies’ optimization scour rate; prediction model; optimization model; MLP model; colliding bodies’ optimization
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Ehteram, M.; Ahmed, A.N.; Ling, L.; Fai, C.M.; Latif, S.D.; Afan, H.A.; Banadkooki, F.B.; El-Shafie, A. Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm. Water 2020, 12, 902.

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