Mir Ajaz Ahmad, Patel Mahesh, Albalawi Fahad, Bajaj Mohit, Tuka Milkias Berhanu
Department of Civil Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, 144011, India.
Department of Electrical Engineering, College of Engineering, Taif University, Taif, 29144, Saudi Arabia.
Sci Rep. 2024 Oct 28;14(1):25725. 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) and root mean square error (RMSE) values vary between various models; however, XGB showed R = 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.
由于复杂的泥沙输运过程和相应的环境因素，准确预测推移质输沙量是水利工程中的一项具有挑战性的任务。当前的研究引入了一种新颖的比较集成方法，该方法使用元启发式机器学习（ML）模型，以利用实验室水槽实验数据提高推移质输沙量预测的准确性。为了揭示推移质输运预测中的关键见解，采用了几种模型，如K近邻（KNN）、极端随机树回归器（ETR）、线性回归（LR）、随机森林（RF）、装袋回归器（BR）和XGBoost（XGB）。不同模型之间的决定系数（R）和均方根误差（RMSE）值有所不同；然而，XGB的R值为0.99，RMSE值为0.11。敏感性分析强调了希尔兹参数在推移质输沙量预测中的关键作用，而SHAP分析突出了XGB模型在提高预测准确性方面的重大影响。REC曲线表明，BR、XGB和RF的表现优于KNN和LR模型。此外，还开发了一个图形用户界面，以促进用户与预测模型的交互，从而更轻松地可视化、分析和解释推移质输运预测。此外，还进行了k折交叉验证，以评估ML模型的性能、一致性和稳健性。因此，从当前研究可以得出结论，ML算法的应用可以提高准确性，为水利工程师提供有价值的见解，并突出ML模型在土木工程实践中的重要性，特别是在推移质输运预测方面。
