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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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2025WR040191 |
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| author | Zechen Peng Shaoxing Mo Alexander Y. Sun Jichun Wu Xiankui Zeng Miao Lu Xiaoqing Shi |
| author_facet | Zechen Peng Shaoxing Mo Alexander Y. Sun Jichun Wu Xiankui Zeng Miao Lu Xiaoqing Shi |
| author_sort | Zechen Peng |
| collection | DOAJ |
| description | 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 Bayesian TimesNet (BTimesNet), a novel deep learning model for probabilistic and explainable GWL prediction. BTimesNet transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. A Bayesian framework using Stein Variational Gradient Descent is implemented to quantify predictive uncertainties. For model interpretability, SHapely Additive exPlanations (SHAP) is utilized to quantify predictor contributions. The efficacy of BTimesNet for multi‐step‐ahead GWL prediction is evaluated using monthly data collected from 19 monitoring wells across three hydroclimatic regions in the U.S., and compared against the widely used long short‐term memory (LSTM) and Autoformer models. Results show that BTimesNet consistently outperforms LSTM and Autoformer, providing more accurate predictions up to 4 months ahead. SHAP analysis reveals that historical GWLs are the most informative features, with meteorological predictors making secondary contributions. BTimesNet's superior performance stems from its ability to extract both short‐ and long‐term temporal features. This approach represents a valuable advancement for accurate GWL prediction and risk‐informed decision‐making, providing critical lead time for proactive groundwater and ecosystem management and agricultural irrigation planning. Its data‐driven nature also facilitates broader applications across hydrological and environmental prediction domains. |
| format | Article |
| id | doaj-art-fbab882d3ea04f878def6713b0cd98d6 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-fbab882d3ea04f878def6713b0cd98d62025-08-20T03:31:30ZengWileyWater Resources Research0043-13971944-79732025-06-01616n/an/a10.1029/2025WR040191An Explainable Bayesian TimesNet for Probabilistic Groundwater Level PredictionZechen Peng0Shaoxing Mo1Alexander Y. Sun2Jichun Wu3Xiankui Zeng4Miao Lu5Xiaoqing Shi6Key Laboratory of Surficial Geochemistry of Ministry of Education School of Earth Sciences and Engineering Nanjing University Nanjing ChinaKey Laboratory of Surficial Geochemistry of Ministry of Education School of Earth Sciences and Engineering Nanjing University Nanjing ChinaBureau of Economic Geology Jackson School of Geosciences University of Texas at Austin Austin TX USAKey Laboratory of Surficial Geochemistry of Ministry of Education School of Earth Sciences and Engineering Nanjing University Nanjing ChinaKey Laboratory of Surficial Geochemistry of Ministry of Education School of Earth Sciences and Engineering Nanjing University Nanjing ChinaCollege of Hydraulic Science and Engineering Yangzhou University Yangzhou ChinaKey Laboratory of Surficial Geochemistry of Ministry of Education School of Earth Sciences and Engineering Nanjing University Nanjing ChinaAbstract 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 Bayesian TimesNet (BTimesNet), a novel deep learning model for probabilistic and explainable GWL prediction. BTimesNet transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. A Bayesian framework using Stein Variational Gradient Descent is implemented to quantify predictive uncertainties. For model interpretability, SHapely Additive exPlanations (SHAP) is utilized to quantify predictor contributions. The efficacy of BTimesNet for multi‐step‐ahead GWL prediction is evaluated using monthly data collected from 19 monitoring wells across three hydroclimatic regions in the U.S., and compared against the widely used long short‐term memory (LSTM) and Autoformer models. Results show that BTimesNet consistently outperforms LSTM and Autoformer, providing more accurate predictions up to 4 months ahead. SHAP analysis reveals that historical GWLs are the most informative features, with meteorological predictors making secondary contributions. BTimesNet's superior performance stems from its ability to extract both short‐ and long‐term temporal features. This approach represents a valuable advancement for accurate GWL prediction and risk‐informed decision‐making, providing critical lead time for proactive groundwater and ecosystem management and agricultural irrigation planning. Its data‐driven nature also facilitates broader applications across hydrological and environmental prediction domains.https://doi.org/10.1029/2025WR040191groundwater level predictionBayesian TimesNetexplainable deep learningprobabilistic prediction |
| spellingShingle | Zechen Peng Shaoxing Mo Alexander Y. Sun Jichun Wu Xiankui Zeng Miao Lu Xiaoqing Shi An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction Water Resources Research groundwater level prediction Bayesian TimesNet explainable deep learning probabilistic prediction |
| title | An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction |
| title_full | An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction |
| title_fullStr | An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction |
| title_full_unstemmed | An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction |
| title_short | An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction |
| title_sort | explainable bayesian timesnet for probabilistic groundwater level prediction |
| topic | groundwater level prediction Bayesian TimesNet explainable deep learning probabilistic prediction |
| url | https://doi.org/10.1029/2025WR040191 |
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