Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sedi...

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Bibliographic Details

Main Authors:	El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.
Format:	Journal Article
Language:	Inglés
Published:	Springer 2024
Subjects:	sediment yield forecasting machine learning algorithms modified universal soil loss equation soil erosion models consistency sensitivity analysis watersheds sediment transport datasets neural networks case studies
Online Access:	https://hdl.handle.net/10568/149323