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...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/149323 |
Ejemplares similares: Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability
- Soil erosion and sediment load management strategies for sustainable irrigation in arid regions
- ¿Puede wepp mejorar la predicción de la erosión de suelos respecto a USLE? = May wepp improve soil erosion prediction compared to usle?
- Universal Soil Loss Equation
- Prediction of sediment yield in a data-scarce river catchment at the sub-basin scale using gridded precipitation datasets
- Genetics of Gel Consistency in Rice (Oryza sativa L.)
- Effect of potato hilling on soil temperature, soil moisture distribution and sediment yield on a sloping terrain