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

El Bilali, Ali; Brouziyne, Youssef; Attar, Oumaima; Lamane, Houda; Hadri, Abdessamad; Taleb, Abdeslam

doi:10.1007/s11356-024-34245-2

Environ Sci Pollut Res

2024

DOI: 10.1007/s11356-024-34245-2

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

Ali El Bilali,

Youssef Brouziyne,

Oumaima Attar

et al.

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A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model

El Bilali,

Taleb

2024

Water Resour Manage

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A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model

El Bilali,

Taleb

2024

Water Resour Manage

View full text Add to dashboard Cite

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

Cited by 1 publication

References 86 publications

A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model

A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model

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