Multi‐Task Deep Learning of Daily Streamflow and Water Temperature. Issue 4 (1st April 2022)

• Record Type: Journal Article
• Authors: Sadler, J. M.; Appling, A. P.; Read, J. S.; Oliver, S. K.; Jia, X.; Zwart, J. A.; Kumar, V.
• Is Part Of: Water resources research. Volume 58:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-01
• Subjects: deep learning -- streamflow prediction -- water temperature prediction -- machine learning; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR030138
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: A single deep learning model was used to predict both water temperature and streamflow The best configured single‐site multi‐task models improved streamflow predictions for most sites tested A naïve implementation of multi‐task learning was detrimental to water temperature predictions