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

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

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...

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主要な著者:	Abid Almubaidin M.A., Latif S.D., Balan K., Ahmed A.N., El-Shafie A.
その他の著者:	58729517300
フォーマット:	論文
出版事項:	Elsevier B.V. 2024
主題:	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
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Enhancing sediment transport predictions through machine learning-based multi-scenario regression models

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