Process‐Guided Deep Learning Predictions of Lake Water Temperature

By Jordan S. Read, Xiaowei Jia, Jared D. Willard, Alison P. Appling, Jacob Aaron Zwart, S. Oliver, Anuj Karpatne, Gretchen J. A. Hansen, Paul C. Hanson, William Watkins and 2 more. Water Resources Research, 2019

329 citations recorded by the source corpus.

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 feedback (model penalties for violating conversation of energy), and model pretraining 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 data set. 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 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 °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.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/bca8e162da23226ca8ca251bef17ce8aada2e940

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Citations in the corpus

Deep Learning for Geophysics: Current and Future Trends (2021, background): “…US (Sun, Scanlon, et al., 2020), measuring accurate river widths by super resolution (Ling et al., 2019), predicting the temperature of lake water (Read et al., 2019), predicting rainfall and runoff (Akbari Asanjan et al., 2018), and prediction water vapor retrieval from remote sensing data…”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, background): “Recurrent [165] Lakes (USA) WT;; NA NA Category 0 1520 samples No details Train:65%, test: 35% LSTM NA”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, methodology): “[231] in particular combined physicsguided loss functions with physics-guided initialization.”