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: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
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| Acceso en línea: | https://hdl.handle.net/10568/149323 |
| _version_ | 1855542172895412224 |
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| author | El Bilali, A. Brouziyne, Youssef Attar, O. Lamane, H. Hadri, A. Taleb, A. |
| author_browse | Attar, O. Brouziyne, Youssef El Bilali, A. Hadri, A. Lamane, H. Taleb, A. |
| author_facet | El Bilali, A. Brouziyne, Youssef Attar, O. Lamane, H. Hadri, A. Taleb, A. |
| author_sort | El Bilali, A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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 sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices. |
| format | Journal Article |
| id | CGSpace149323 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1493232025-12-08T09:54:28Z Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability El Bilali, A. Brouziyne, Youssef Attar, O. Lamane, H. Hadri, A. Taleb, A. 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 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 sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices. 2024-07 2024-07-31T06:05:15Z 2024-07-31T06:05:15Z Journal Article https://hdl.handle.net/10568/149323 en Limited Access Springer El Bilali, A.; Brouziyne, Youssef; Attar, O.; Lamane, H.; Hadri, A.; Taleb, A. 2024. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability. Environmental Science and Pollution Research, 31(34):47237-47257. [doi: https://doi.org/10.1007/s11356-024-34245-2] |
| spellingShingle | 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 El Bilali, A. Brouziyne, Youssef Attar, O. Lamane, H. Hadri, A. Taleb, A. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title | Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title_full | Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title_fullStr | Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title_full_unstemmed | Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title_short | Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability |
| title_sort | physics informed machine learning algorithms for forecasting sediment yield an analysis of physical consistency sensitivity and interpretability |
| topic | 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 |
| url | https://hdl.handle.net/10568/149323 |
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