| Autor: |
Jeffrey M. Sadler, Jordan S. Read, Alison P. Appling, Vipin Kumar, Xiaowei Jia, Jacob A. Zwart, Samantha K. Oliver |
| Rok vydání: |
2021 |
| Předmět: |
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| Popis: |
Deep learning models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi-task deep learning. A multi-task scaling factor controlled the relative contribution of the auxiliary variable’s error to the overall loss during training. Our experiments examined the improvement in prediction accuracy of the multi-task approach using paired streamflow and temperature data from sites across the conterminous United States. Our results showed that the best performing multi-task models performed better overall than the single-task models in terms of Nash-Sutcliffe efficiency. The improvement of the multi-task models relative to the single-task models had a seasonal trend with the multi-task models making larger improvements in the high-flow seasons. The multi-task scaling factor was consequential in determining to what extent the multi-task approach was beneficial and a naive selection of this factor led to worse-performing multi-task models for stream temperature. Our findings indicate that, when configured properly, a multi-task approach could make more accurate predictions of interdependent hydrologic variables. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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