Developing an explainable deep learning module based on the LSTM framework for flood prediction
Long short-term memory (LSTM) networks have become indispensable tools in hydrological modeling due to their ability to capture long-term dependencies, handle non-linear relationships, and integrate multiple data sources but suffer from limited interpretability due to their black box nature. To addr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Water |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frwa.2025.1562842/full |
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