Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models
Considering the differences and complex nonlinear relationships of the observational data, this research integrated the hydrological, hydrodynamic and time series models, including the SWAT+, MIKE21, VMD, SARIMA, TCN and ADPSO, to increase the accuracy and efficiency of streamflow simulations by app...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2387051 |
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