Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process‐Based Modeling With Deep Learning

Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process‐Based Modeling With Deep Learning

Abstract Hybrid Knowledge‐Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process‐based model simulations, have shown improved performance over their process‐based counterparts for the simulation of water temperature and hydrodynamics. We high...

Full description

Bibliographic Details
Main Authors: R. Ladwig, A. Daw, E. A. Albright, C. Buelo, A. Karpatne, M. F. Meyer, A. Neog, P. C. Hanson, H. A. Dugan
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-01-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
hydrodynamics
lake model
deep learning
knowledge‐guided machine learning
water temperature
modular compositional learning
Online Access:https://doi.org/10.1029/2023MS003953

Internet

https://doi.org/10.1029/2023MS003953

Similar Items