Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models

Significant attention has recently been paid to deep learning as a method for improved catchment modeling. Compared with process‐based models, deep learning is often criticized for its lack of interpretability. One solution is to combine a process‐based hydrological model with a residual error model...

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Veröffentlicht in:	Water resources research Jg. 57; H. 9
Hauptverfasser:	Li, Dayang, Marshall, Lucy, Liang, Zhongmin, Sharma, Ashish, Zhou, Yan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington John Wiley & Sons, Inc 01.09.2021
Schlagworte:	Artificial neural networks Bayesian analysis Bayesian inference Bayesian theory Catchment models Catchments Deep learning Errors Groundwater Groundwater models Hydrologic models Hydrologic processes hydrological model Hydrology Intervals LSTM Machine learning Markov chain Markov chains Mathematical models MIKE SHE model uncertainty Modelling Natural language processing Neural networks Parameters probabilistic prediction Probability theory regression analysis Regression models Reliability Sharpness Statistical analysis Statistical inference Statistical methods stochastic variational inference Uncertainty Watersheds
ISSN:	0043-1397, 1944-7973
Online-Zugang:	Volltext
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