Prediction of River Pipeline Scour Depth Using Machine Learning Approaches
Year: 2025
Type: article
Abstract: The precise assessment of local scour depth under pipelines is a complicated occurrence, and the definitive method for its calculation remains unclear. This work tackles the issue by using computational models to accurately predict scour depth with great reliability. A support vector machine (SVM) was used to forecast pipeline scour depth, using an extensive dataset. The results were juxtaposed with typical datasets andother prediction methodologies, including regression equations and Radial Basis Function Neural Networks (RBFNN). The comparison study indicates that the SVM surpasses conventional regression techniques and RBFNN, attaining a superior generalization capability with R² = 0.89, RMSE = 0.046, MAE = 0.32%, and δ = 9.9. Principal results indicate that the mean diameter of particles substantially affects scour depth, while flow discharge has no effect. Non-dimensional metrics, like the Shields parameter, are essential in assessing scour depth. These findings underscore the efficacy of SVM in precisely forecasting scour depth under pipelines, making it an indispensable instrument for hydraulic engineering applications. This paper introduces a uniquecontribution to scouring estimate approaches by using both dimensional and non-dimensional datasets, establishing a baseline for future research in this field.
Authors: KAUSHIK V, KULKARNI K. H, TRIPATHI R. P, RAMDEEN R. J, Ryan Rampair, ALI A., MOHDYUSOFF MA, CHABOKPOUR J, Sheo P S, Pandey S
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6930737604
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