Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm

Mohammad Ehteram, Ali Najah Ahmed, Lloyd Ling, Chow Ming Fai, Sarmad Dashti Latif, Haitham Abdulmohsin Afan, Fatemeh Barzegari Banadkooki, Ahmed El-Shafie

Research output: Contribution to journalArticleResearchpeer-review

34 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number902
Number of pages20
JournalWater
Volume12
Issue number3
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes

Keywords

  • Colliding bodies' optimization
  • MLP model
  • Optimization model
  • Prediction model
  • Scour rate

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