Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-823-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-823-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Mar 2021
Research article |
| 02 Mar 2021
| 02 Mar 2021
Land subsidence due to groundwater pumping: hazard probability assessment through the combination of Bayesian model and fuzzy set theory
Huijun Li
Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory
of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing, 100048, China
Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory
of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing, 100048, China
Gaoxuan Guo
Beijing Institute of Hydrogeology and Engineering Geology, Beijing,
China
Yan Zhang
Key Laboratory of Earth Fissures Geological Disaster, Ministry of
Natural Resources, Geological Survey of Jiangsu Province, Jiangsu, China
Zhenxue Dai
College of Construction Engineering, Jilin University, Changchun,
130026, China
Xiaojuan Li
Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Key Laboratory
of 3-Dimensional Information Acquisition and Application, Capital Normal University, Beijing, 100048, China
Linzhen Chang
Fourth Institute of Hydrogeology and Engineering Geology, Hebei Bureau of Geology and Mineral Resources Exploration, Hebei, China
Pietro Teatini
Department of Civil, Environmental and Architectural Engineering, University of Padua, Padua 35121, Italy
Land Subsidence International Initiative (UNESCO LaSII),
Querétaro, Mexico
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Cited
17 citations as recorded by crossref.
- Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia A. Yananto et al. 10.1007/s12145-024-01413-4
- Unraveling the Deformation and Water Storage Characteristics of Different Aquifer Groups by Integrating PS-InSAR Technology and a Spatial Correlation Model D. Zhao et al. 10.1109/JSTARS.2023.3323699
- Effect of movability of water on the low-velocity pre-Darcy flow in clay soil H. Cheng et al. 10.1016/j.jrmge.2023.12.007
- Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR H. Li et al. 10.1016/j.scitotenv.2021.149244
- Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy E. Ghaderpour et al. 10.1016/j.rsase.2024.101191
- Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework H. Sadeghi et al. 10.1016/j.rsase.2023.100989
- Numerical simulation and verification of goaf morphology evolution and surface subsidence in a mine L. He et al. 10.1016/j.engfailanal.2022.106918
- Land subsidence prediction in Zhengzhou's main urban area using the GTWR and LSTM models combined with the Attention Mechanism Y. Yuan et al. 10.1016/j.scitotenv.2023.167482
- Aquifer system deformation in the San Luis Valley: A new framework for modeling subsidence in agricultural regions S. Vajedian et al. 10.1016/j.jhydrol.2024.131876
- Research on the Prediction Model of Mine Subsidence Based on Object-Oriented and Probability Integration Method Z. Gu et al. 10.1155/2022/8107024
- Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence B. Zhao et al. 10.3390/app14188330
- Assessment of coal mining land subsidence by using an innovative comprehensive weighted cloud model combined with a PSR conceptual model C. Xu et al. 10.1007/s11356-021-17052-x
- Land deformation and sinkhole occurrence in response to the fluctuations of groundwater storage: an integrated assessment of GRACE gravity measurements, ICESat/ICESat-2 altimetry data, and hydrologic models B. Khorrami et al. 10.1080/15481603.2021.2000349
- Mapping land subsidence susceptibility due to groundwater decline using fuzzy pixel-based models M. Aalipour et al. 10.1007/s12517-022-10269-1
- A Numerical Assessment and Prediction for Meeting the Demand for Agricultural Water and Sustainable Development in Irrigation Area Q. Zhang et al. 10.3390/rs15030571
- Geoelectrical Characterization of Coastal Aquifers in Agbado-Ijaye, Lagos, Southwestern Nigeria; Implications for Groundwater Resources Sustainability K. Oyeyemi et al. 10.3390/su15043538
- Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors S. Ghoushchi et al. 10.1016/j.heliyon.2024.e29415
17 citations as recorded by crossref.
- Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia A. Yananto et al. 10.1007/s12145-024-01413-4
- Unraveling the Deformation and Water Storage Characteristics of Different Aquifer Groups by Integrating PS-InSAR Technology and a Spatial Correlation Model D. Zhao et al. 10.1109/JSTARS.2023.3323699
- Effect of movability of water on the low-velocity pre-Darcy flow in clay soil H. Cheng et al. 10.1016/j.jrmge.2023.12.007
- Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR H. Li et al. 10.1016/j.scitotenv.2021.149244
- Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy E. Ghaderpour et al. 10.1016/j.rsase.2024.101191
- Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework H. Sadeghi et al. 10.1016/j.rsase.2023.100989
- Numerical simulation and verification of goaf morphology evolution and surface subsidence in a mine L. He et al. 10.1016/j.engfailanal.2022.106918
- Land subsidence prediction in Zhengzhou's main urban area using the GTWR and LSTM models combined with the Attention Mechanism Y. Yuan et al. 10.1016/j.scitotenv.2023.167482
- Aquifer system deformation in the San Luis Valley: A new framework for modeling subsidence in agricultural regions S. Vajedian et al. 10.1016/j.jhydrol.2024.131876
- Research on the Prediction Model of Mine Subsidence Based on Object-Oriented and Probability Integration Method Z. Gu et al. 10.1155/2022/8107024
- Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence B. Zhao et al. 10.3390/app14188330
- Assessment of coal mining land subsidence by using an innovative comprehensive weighted cloud model combined with a PSR conceptual model C. Xu et al. 10.1007/s11356-021-17052-x
- Land deformation and sinkhole occurrence in response to the fluctuations of groundwater storage: an integrated assessment of GRACE gravity measurements, ICESat/ICESat-2 altimetry data, and hydrologic models B. Khorrami et al. 10.1080/15481603.2021.2000349
- Mapping land subsidence susceptibility due to groundwater decline using fuzzy pixel-based models M. Aalipour et al. 10.1007/s12517-022-10269-1
- A Numerical Assessment and Prediction for Meeting the Demand for Agricultural Water and Sustainable Development in Irrigation Area Q. Zhang et al. 10.3390/rs15030571
- Geoelectrical Characterization of Coastal Aquifers in Agbado-Ijaye, Lagos, Southwestern Nigeria; Implications for Groundwater Resources Sustainability K. Oyeyemi et al. 10.3390/su15043538
- Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors S. Ghoushchi et al. 10.1016/j.heliyon.2024.e29415
Latest update: 20 Nov 2024
Short summary
We propose a method that integrates fuzzy set theory and a weighted Bayesian model to evaluate the hazard probability of land subsidence based on Interferometric Synthetic Aperture Radar technology. The proposed model can represent the uncertainty and ambiguity in the evaluation process, and results can be compared to traditional qualitative methods.
We propose a method that integrates fuzzy set theory and a weighted Bayesian model to evaluate...
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