TY - JOUR
T1 - Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm
AU - Ehteram, Mohammad
AU - Ahmed, Ali Najah
AU - Ling, Lloyd
AU - Fai, Chow Ming
AU - Latif, Sarmad Dashti
AU - Afan, Haitham Abdulmohsin
AU - Banadkooki, Fatemeh Barzegari
AU - El-Shafie, Ahmed
N1 - Funding Information:
Funding: The authors appreciate the financial support received from the research grant coded GPD082A-2018 funded by the University of Malaya and from IPSR of the Universiti Tunku Abdul Rahman.
Publisher Copyright:
© 2020 by the authors.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
KW - Colliding bodies' optimization
KW - MLP model
KW - Optimization model
KW - Prediction model
KW - Scour rate
UR - http://www.scopus.com/inward/record.url?scp=85082559358&partnerID=8YFLogxK
U2 - 10.3390/w12030902
DO - 10.3390/w12030902
M3 - Article
AN - SCOPUS:85082559358
SN - 2073-4441
VL - 12
JO - Water
JF - Water
IS - 3
M1 - 902
ER -