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| Created: | Aug 18, 2025 at 9:05 p.m. (UTC) | |
| Last updated: | Aug 18, 2025 at 11:10 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Discoverable |
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Abstract
We aim to integrate stream thermal buffering capacity with machine learning models to simulate summer stream temperature dynamics and evaluate model performance. Using paired air and stream temperature data, we quantified each stream's thermal
buffering capacity. We then developed Long Short-Term Memory (LSTM) and Random Forest models to predict daily summer stream temperatures.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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