Enhancing sediment transport predictions through machine learning-based multi-scenario regression models

• Main Authors: Abid Almubaidin M.A., Latif S.D., Balan K., Ahmed A.N., El-Shafie A.
• Other Authors: 58729517300
• Format: Article
• Published: Elsevier B.V. 2024
• Subjects: ediment transport modelling; Ensemble of trees; Gaussian process regression; Kernel approximation; Linear regression; Regression trees; Support vector machines; Forecasting; Gaussian distribution; Gaussian noise (electronic); Learning systems; Mean square error; Sediment transport; Sedimentation; Suspended sediments; Ediment transport modeling; Ensemble of tree; Machine-learning; Percentage error; Regression modelling; Support vectors machine; Transport modelling

Machine learning is one effective way of increasing the accuracy of sediment transport models. Machine learning captures patterns in the sequence of both structured and unstructured data and uses it for forecasting. In this research, the different regression models were train to forecast sediment da...