Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Abstract Ensemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning...

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Bibliographic Details
Main Authors: Alexander Y. Sun, Peishi Jiang, Pin Shuai, Xingyuan Chen
Format: Article
Language:English
Published: Wiley 2024-10-01
Series:Water Resources Research
Subjects:
neural operator learning
DeepONet
ensemble simulation
streamflow forecasting
hybrid machine learning
uncertainty quantification
Online Access:https://doi.org/10.1029/2024WR037555
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https://doi.org/10.1029/2024WR037555

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