An efficient parallel runoff forecasting model for capturing global and local feature information
Abstract Artificial intelligence has significantly accelerated the development of hydrological forecasting. However, research on how to efficiently identify the physical characteristics of runoff sequences and develop forecasting models that simultaneously address both global and local features of t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96940-5 |
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