Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

Ahmad Sharafati, Ali Tafarojnoruz, Davide Motta, Zaher Mundher Yaseen

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization , ant colony (), differential evolution and genetic algorithm () and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (), Keulegan–Carpenter number () and embedded depth to diameter of pipe ratio () are considered as prediction variables. Results indicate that the model ( and ) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination than it is due to the model structure .
Original languageEnglish
Pages (from-to)1425-1451
Number of pages27
JournalJournal of Hydroinformatics
Volume22
Issue number6
Early online date20 Aug 2020
DOIs
Publication statusPublished - 1 Nov 2020

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