Process‐guided deep learning predictions of lake water temperature

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...

Categories: Publication; Types: Citation; Tags: Fish, Northeast CASC, Rivers, Streams and Lakes, Water, Coasts and Ice, Wildlife and Plants, All tags... data science, deep learning, lake modeling, process‐guided deep learning, temperature prediction, theory‐guided data science, Fewer tags