Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locatio...

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Bibliographic Details
Main Authors: Jacob A. Zwart, Jeremy Diaz, Scott Hamshaw, Samantha Oliver, Jesse C. Ross, Margaux Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan Read, Jeffrey Sadler, Theodore Thompson, David Watkins, Elaheh White
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Water
Subjects:
deep learning—artificial neural network
forecast
data assimilation
water temperature
unmonitored catchment
Online Access:https://www.frontiersin.org/articles/10.3389/frwa.2023.1184992/full

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https://www.frontiersin.org/articles/10.3389/frwa.2023.1184992/full

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