Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions

Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions

Abstract Hydrogeological modeling is essential for managing groundwater systems, especially in the transport and remediation of contaminants. Traditional modeling methods face challenges due to the increasing complexity and volume of data. Deep learning (DL) has emerged as a promising tool, offering...

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Bibliographic Details
Main Authors: Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll
Format: Article
Language:English
Published: Wiley 2025-06-01
Series:Journal of Geophysical Research: Machine Learning and Computation
Subjects:
hydrogeological modeling
deep learning
data availability
model interpretability
computational complexity
Online Access:https://doi.org/10.1029/2025JH000703
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