Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning
Abstract Accurate forecasting for groundwater levels is essential for water resource management and sustainable development. Regional variations in groundwater levels exhibit a complex spatial dependency structure due to the physical proximity of monitoring wells, hydrological connectivity, and shar...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024JH000520 |
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| _version_ | 1850167598508933120 |
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| author | Yinghan Wu Gang Mei Kaixuan Shao Nengxiong Xu Jianbing Peng |
| author_facet | Yinghan Wu Gang Mei Kaixuan Shao Nengxiong Xu Jianbing Peng |
| author_sort | Yinghan Wu |
| collection | DOAJ |
| description | Abstract Accurate forecasting for groundwater levels is essential for water resource management and sustainable development. Regional variations in groundwater levels exhibit a complex spatial dependency structure due to the physical proximity of monitoring wells, hydrological connectivity, and shared environmental characteristics. However, existing research has mostly overlooked the multiple spatial dependencies between monitoring wells, limiting the understanding of the added value that graph‐based models bring to groundwater dynamics prediction. In this study, we characterize spatial dependencies of groundwater from multiple perspectives and investigate the impact on the forecasting results of groundwater dynamics using graph‐based deep learning. Characterizing spatial dependencies helps improve the understanding of groundwater dynamics, but its effectiveness in enhancing prediction accuracy depends on the characteristics of spatial interactions. Graph neural networks facilitate learning from diverse spatial associations, allowing for a more comprehensive representation of spatial dependencies and uncovering potential connections between monitoring wells. Results based on real data sets contribute to understanding how multiple spatial dependencies influence groundwater level forecasting. Our research provides insights into exploiting potential spatial dependencies in similarly complex and highly interconnected earth systems. |
| format | Article |
| id | doaj-art-e8be9c5beec641bcbd4b0792141e5788 |
| institution | OA Journals |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-e8be9c5beec641bcbd4b0792141e57882025-08-20T02:21:10ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2024JH000520Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep LearningYinghan Wu0Gang Mei1Kaixuan Shao2Nengxiong Xu3Jianbing Peng4School of Engineering and Technology China University of Geosciences Beijing ChinaSchool of Engineering and Technology China University of Geosciences Beijing ChinaSchool of Engineering and Technology China University of Geosciences Beijing ChinaSchool of Engineering and Technology China University of Geosciences Beijing ChinaSchool of Engineering and Technology China University of Geosciences Beijing ChinaAbstract Accurate forecasting for groundwater levels is essential for water resource management and sustainable development. Regional variations in groundwater levels exhibit a complex spatial dependency structure due to the physical proximity of monitoring wells, hydrological connectivity, and shared environmental characteristics. However, existing research has mostly overlooked the multiple spatial dependencies between monitoring wells, limiting the understanding of the added value that graph‐based models bring to groundwater dynamics prediction. In this study, we characterize spatial dependencies of groundwater from multiple perspectives and investigate the impact on the forecasting results of groundwater dynamics using graph‐based deep learning. Characterizing spatial dependencies helps improve the understanding of groundwater dynamics, but its effectiveness in enhancing prediction accuracy depends on the characteristics of spatial interactions. Graph neural networks facilitate learning from diverse spatial associations, allowing for a more comprehensive representation of spatial dependencies and uncovering potential connections between monitoring wells. Results based on real data sets contribute to understanding how multiple spatial dependencies influence groundwater level forecasting. Our research provides insights into exploiting potential spatial dependencies in similarly complex and highly interconnected earth systems.https://doi.org/10.1029/2024JH000520groundwater level forecastingmultiple spatial dependenciesgraph‐based deep learningtemporal graph convolutional network |
| spellingShingle | Yinghan Wu Gang Mei Kaixuan Shao Nengxiong Xu Jianbing Peng Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning Journal of Geophysical Research: Machine Learning and Computation groundwater level forecasting multiple spatial dependencies graph‐based deep learning temporal graph convolutional network |
| title | Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning |
| title_full | Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning |
| title_fullStr | Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning |
| title_full_unstemmed | Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning |
| title_short | Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning |
| title_sort | forecasting groundwater level by characterizing multiple spatial dependencies of environmental factors using graph based deep learning |
| topic | groundwater level forecasting multiple spatial dependencies graph‐based deep learning temporal graph convolutional network |
| url | https://doi.org/10.1029/2024JH000520 |
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