Item talk:Q308420
From geokb
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"name": "Process-guided deep learning predictions of lake water temperature",
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"value": "10.1029/2019WR024922",
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"name": "Water Resources Research",
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"abstract": "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\u2010of\u2010the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process\u2010Guided Deep Learning (PGDL) hybrid modeling framework with a use\u2010case of predicting depth\u2010specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short\u2010term memory recurrence), theory\u2010based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process\u2010based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process\u2010based (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 data set. The PGDL model performance (as measured by root\u2010mean\u2010square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 \u00b0C during the test period (DL: 1.78 \u00b0C, PB: 2.03 \u00b0C; in a small number of lakes PB or DL models were more accurate). This case\u2010study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.",
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