Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature
Precise river water temperature (WT) forecasts are essential for monitoring water quality. This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2450477 |
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