Deep learning (DL) 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 DL. 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 water temperature data from sites across the conterminous United States. Our results showed that for 56 out of 101 sites, the best performing multi-task models performed better overall than the single-task models in terms of Nash-Sutcliffe efficiency for predicting streamflow with single-site models. For 43 sites, the best multi-task, single-site models made no significant difference in predicting streamflow. The multi-task approach had a smaller effect when applied to a model trained with data from 101 sites together, significantly improving performance for only 17 sites. The multi-task scaling factor was consequential in determining to what extent the multi-task approach was beneficial. A naïve selection of this factor led to significantly worse-performing models for 3 of 101 sites when predicting streamflow as the primary variable, and 47 of 53 sites when predicting stream temperature as the primary variable. We conclude that a multi-task approach can make more accurate predictions by leveraging information from interdependent hydrologic variables, but only for some sites, variables, and model configurations.
Citation Information
Publication Year | 2022 |
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Title | Multi-task deep learning of daily streamflow and water temperature |
DOI | 10.1029/2021WR030138 |
Authors | Jeffrey Michael Sadler, Alison P. Appling, Jordan Read, Samantha K. Oliver, Xiaowei Jia, Jacob Aaron Zwart, Vipin Kumar |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Water Resources Research |
Index ID | 70221284 |
Record Source | USGS Publications Warehouse |
USGS Organization | WMA - Integrated Information Dissemination Division |