A comparative ensemble approach to bedload prediction using metaheuristic machine learning

• Published in: Scientific reports Vol. 14; no. 1; pp. 25725 - 22
• Main Authors: Mir, Ajaz Ahmad, Patel, Mahesh, Albalawi, Fahad, Bajaj, Mohit, Tuka, Milkias Berhanu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/986; 639/705; 639/705/1041; 639/705/117; Accuracy; Alluvial channels; Bed load; Bedload transport; Civil engineering; Environmental factors; Humanities and Social Sciences; Hydraulic engineering; Learning algorithms; Machine learning; Mathematical models; multidisciplinary; Performance assessment; Prediction; Prediction models; Science; Science (multidisciplinary); Sediment transport; Sensitivity analysis; Transport processes; Bedload transport; Alluvial channels; Prediction; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75118-5

An adequate bedload prediction is a challenging task in hydraulic engineering because of the complex sediment transport processes and corresponding environmental factors. The current study introduces a novel comparative ensemble approach using metaheuristic machine learning (ML) models to enhance the accuracy of bedload prediction using data from laboratory flume experiments. To uncover key insights in bedload transport prediction, several models are employed, such as K-Nearest Neighbours (KNN), Extra Trees Regressor (ETR), Linear Regression (LR), Random Forest (RF), Bagging Regressor (BR), and XGBoost (XGB). The coefficient of determination ( R 2 ) and root mean square error ( RMSE ) values vary between various models; however, XGB showed R 2  = 0.99 and RMSE = 0.11. The sensitivity analysis emphasizes the crucial role of the Shields parameter in bedload prediction, while the SHAP analysis highlights the substantial influence of the XGB model in enhancing predictive accuracy. The REC curves show that BR, XGB, and RF, outperformed KNN and LR models. Furthermore, a graphical user interface has also been developed to facilitate user interaction with the predictive models, allowing for easier visualization, analysis, and interpretation of bedload transport predictions. Additionally, k-fold cross-validation was performed to assess the performance, consistency and robustness of the ML models. Thus, it can be concluded from the current study that the utilization of ML algorithms can improve accuracy, providing valuable insights for hydraulic engineers and highlighting the importance of ML models in civil engineering practices, particularly in bedload transport prediction.