Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm
Forecasting; Multilayers; Particle swarm optimization (PSO); Pipelines; Soft computing; Colliding bodies; MLP model; Multi layer perceptron; Optimization algorithms; Optimization modeling; Prediction model; Soft computing models; Wave characteristics; Scour; algorithm; hydrological modeling; model;...
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2023
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my.uniten.dspace-255552023-05-29T16:10:51Z Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm Ehteram M. Ahmed A.N. Ling L. Fai C.M. Latif S.D. Afan H.A. Banadkooki F.B. El-Shafie A. 57113510800 57214837520 56203785300 57214146115 57216081524 56436626600 57201068611 16068189400 Forecasting; Multilayers; Particle swarm optimization (PSO); Pipelines; Soft computing; Colliding bodies; MLP model; Multi layer perceptron; Optimization algorithms; Optimization modeling; Prediction model; Soft computing models; Wave characteristics; Scour; algorithm; hydrological modeling; model; optimization; pipeline; scour; Cetacea 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. � 2020 by the authors. Final 2023-05-29T08:10:50Z 2023-05-29T08:10:50Z 2020 Article 10.3390/w12030902 2-s2.0-85082559358 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082559358&doi=10.3390%2fw12030902&partnerID=40&md5=871ded5aadbf59645df2e3d110c3b80f https://irepository.uniten.edu.my/handle/123456789/25555 12 3 902 All Open Access, Gold MDPI AG Scopus |
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| description |
Forecasting; Multilayers; Particle swarm optimization (PSO); Pipelines; Soft computing; Colliding bodies; MLP model; Multi layer perceptron; Optimization algorithms; Optimization modeling; Prediction model; Soft computing models; Wave characteristics; Scour; algorithm; hydrological modeling; model; optimization; pipeline; scour; Cetacea |
| author2 |
57113510800 |
| author_facet |
57113510800 Ehteram M. Ahmed A.N. Ling L. Fai C.M. Latif S.D. Afan H.A. Banadkooki F.B. El-Shafie A. |
| format |
Article |
| author |
Ehteram M. Ahmed A.N. Ling L. Fai C.M. Latif S.D. Afan H.A. Banadkooki F.B. El-Shafie A. |
| spellingShingle |
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 |
| author_sort |
Ehteram M. |
| title |
Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| title_short |
Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| title_full |
Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| title_fullStr |
Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| title_full_unstemmed |
Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| title_sort |
pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806425575269597184 |
| score |
13.222552 |