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

• Published in: Journal of hydroinformatics Vol. 22; no. 6; pp. 1425 - 1451
• Main Authors: Sharafati, Ahmad, Tafarojnoruz, Ali, Motta, Davide, Yaseen, Zaher Mundher
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.11.2020
• Subjects: Adaptive systems; Algorithms; Ant colony optimization; Artificial neural networks; Depth; Diameters; Dimensional analysis; Evolutionary algorithms; Evolutionary computation; Fuzzy logic; Genetic algorithms; Hydraulics; Laboratories; Mathematical models; Optimization algorithms; Parameters; Particle swarm optimization; Performance prediction; Pipeline design; Pipelines; Prediction models; Reynolds number; Rivers; Scour; Scouring; Sediments; Shear stress; Submarine pipelines; Uncertainty; Uncertainty analysis; Water depth
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2020.184

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 .