Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington

Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington

The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However...

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Bibliographic Details
Main Authors: Junqi Zhang, Jing Li, Huiyizhe Zhao, Wen Wang, Na Lv, Bowen Zhang, Yue Liu, Xinyu Yang, Mengjing Guo, Yuhao Dong
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Atmosphere
Subjects:
runoff simulation
hydrological model
machine learning
SIMHYD
LSTM
US CAMELS dataset
Online Access:https://www.mdpi.com/2073-4433/15/12/1461
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