Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area
IntroductionSurface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1503634/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841543778698002432 |
|---|---|
| author | Jiaer Yang Jiaer Yang Pinglang Kou Pinglang Kou Xu Dong Xu Dong Ying Xia Qinchuan Gu Yuxiang Tao Jiangfan Feng Qin Ji Weizao Wang Ram Avtar |
| author_facet | Jiaer Yang Jiaer Yang Pinglang Kou Pinglang Kou Xu Dong Xu Dong Ying Xia Qinchuan Gu Yuxiang Tao Jiangfan Feng Qin Ji Weizao Wang Ram Avtar |
| author_sort | Jiaer Yang |
| collection | DOAJ |
| description | IntroductionSurface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.MethodsSBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models—Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.ResultsDeformation rates for riverside urban ground, road slopes, and ancient landslides were −3.48 ± 2.91 mm/yr, −5.19 ± 3.62 mm/yr, and −6.02 ± 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.DiscussionReservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.ConclusionsThis study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks. |
| format | Article |
| id | doaj-art-a11aaefe7ca845da8071786fbf1b0acc |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-a11aaefe7ca845da8071786fbf1b0acc2025-01-13T06:10:47ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011210.3389/feart.2024.15036341503634Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir areaJiaer Yang0Jiaer Yang1Pinglang Kou2Pinglang Kou3Xu Dong4Xu Dong5Ying Xia6Qinchuan Gu7Yuxiang Tao8Jiangfan Feng9Qin Ji10Weizao Wang11Ram Avtar12Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaKey Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Key Laboratory of GIS Application, School of Geography and Tourism, Chongqing Normal University, Chongqing, ChinaCollege of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, ChinaInstitute of Satellite Remote Sensing and Geographic Information Systems, Hokkaido University, Hokkaido, JapanIntroductionSurface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.MethodsSBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models—Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.ResultsDeformation rates for riverside urban ground, road slopes, and ancient landslides were −3.48 ± 2.91 mm/yr, −5.19 ± 3.62 mm/yr, and −6.02 ± 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.DiscussionReservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.ConclusionsThis study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.https://www.frontiersin.org/articles/10.3389/feart.2024.1503634/fullsurface deformationSBAS-InSARThree Gorges Reservoir areamachine learning predictionreservoir water level impact |
| spellingShingle | Jiaer Yang Jiaer Yang Pinglang Kou Pinglang Kou Xu Dong Xu Dong Ying Xia Qinchuan Gu Yuxiang Tao Jiangfan Feng Qin Ji Weizao Wang Ram Avtar Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area Frontiers in Earth Science surface deformation SBAS-InSAR Three Gorges Reservoir area machine learning prediction reservoir water level impact |
| title | Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area |
| title_full | Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area |
| title_fullStr | Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area |
| title_full_unstemmed | Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area |
| title_short | Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area |
| title_sort | reservoir water level decline accelerates ground subsidence insar monitoring and machine learning prediction of surface deformation in the three gorges reservoir area |
| topic | surface deformation SBAS-InSAR Three Gorges Reservoir area machine learning prediction reservoir water level impact |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1503634/full |
| work_keys_str_mv | AT jiaeryang reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT jiaeryang reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT pinglangkou reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT pinglangkou reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT xudong reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT xudong reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT yingxia reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT qinchuangu reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT yuxiangtao reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT jiangfanfeng reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT qinji reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT weizaowang reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea AT ramavtar reservoirwaterleveldeclineacceleratesgroundsubsidenceinsarmonitoringandmachinelearningpredictionofsurfacedeformationinthethreegorgesreservoirarea |