An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction

An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction

Abstract Reliable groundwater level (GWL) prediction is essential for sustainable water resources management. Despite recent advances in machine learning (ML) methods for GWL prediction, further improvements may be made in uncertainty quantification and model interpretability. This study proposes Ba...

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Bibliographic Details
Main Authors: Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Water Resources Research
Subjects:
groundwater level prediction
Bayesian TimesNet
explainable deep learning
probabilistic prediction
Online Access:https://doi.org/10.1029/2025WR040191
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https://doi.org/10.1029/2025WR040191

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