Process‐guided deep learning predictions of lake water temperature
Dates
Publication Date
2019-11-08
Citation
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S. K., et al ( 2019). Process‐guided deep learning predictions of lake water temperature. Water Resources Research, 55. https://doi.org/10.1029/2019WR024922.
Summary
Abstract(from:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024922)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedbacks (model penalties for violating conversation [...]
Summary
Abstract(from:https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024922)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedbacks (model penalties for violating conversation of energy), and model pre‐training to initialize the network with synthetic data (water temperature predictions from a process‐based model). In‐situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training dataset. The PGDL model performance (as measured by Root Mean Square Error; RMSE) was superior to DL and PB for two detailed study lakes, but only when pre‐training data included greater variability than the training period. The PGDL model also performed well when extended to sixty‐eight lakes, with a median RMSE of 1.65°C during the test period (DL: 1.78°C; PB: 2.03°C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
