Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis
Abstract Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological ana...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87789-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571799518314496 |
|---|---|
| author | Zheng Zhou Jiyuan Hu Jiayao Wang Lijun Wang Tianrong Qiao Zhen Li Shiyuan Cheng |
| author_facet | Zheng Zhou Jiyuan Hu Jiayao Wang Lijun Wang Tianrong Qiao Zhen Li Shiyuan Cheng |
| author_sort | Zheng Zhou |
| collection | DOAJ |
| description | Abstract Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. The findings detected three major subsidence zones and two uplift zones within the city. The most significant subsidence occurred in the northern part of Zhongmu (− 28 mm/year), the northwest of Xingyang (− 16 mm/year), and the western region of Gongyi (− 6 mm/year). Conversely, a notable uplift was observed in the central city district (13 mm/year) and Xinzheng Airport (12 mm/year). The accuracy assessment of in-situ measurements (GNSS and levelling) yielded an overall root-mean-square error (RMSE) of 2.2 mm/year and an R-square of 0.948. Subsequently, the feature evaluation results based on the XGBoost method suggest that road density and precipitation are the dominant factors affecting land deformation in the entire study area or in the subsidence and uplift zones individually. Nevertheless, the other five factors (groundwater storage, soil type, soil thickness, NDVI, and slope) also act on land deformation individually and are intricately intertwined with each other. Furthermore, hydrogeological analysis from six groundwater wells reveals a synchronous relationship between groundwater level decline and land subsidence. The building load analysis shows a significant correlation between build-up density and subsidence rates, especially for those severe subsidence areas, with the maximum correlation coefficient reaching 0.6312. Finally, the geographic patterns analysis of post-event demonstrated a northeastward trend in land deformation, with a gradual reduction of deformation impact from 2018 to 2022. |
| format | Article |
| id | doaj-art-7e956d158be9497fb6e9c2dfe2cb872a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7e956d158be9497fb6e9c2dfe2cb872a2025-02-02T12:18:46ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-87789-9Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysisZheng Zhou0Jiyuan Hu1Jiayao Wang2Lijun Wang3Tianrong Qiao4Zhen Li5Shiyuan Cheng6College of Geography and Environmental Science, Henan UniversityCollege of Geography and Environmental Science, Henan UniversityCollege of Geography and Environmental Science, Henan UniversityCollege of Geography and Environmental Science, Henan UniversityHenan Academy of GeologyCollege of Surveying and Geo-informatics, North China University of Water Resources and Electric PowerCollege of Geography and Environmental Science, Henan UniversityAbstract Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. The findings detected three major subsidence zones and two uplift zones within the city. The most significant subsidence occurred in the northern part of Zhongmu (− 28 mm/year), the northwest of Xingyang (− 16 mm/year), and the western region of Gongyi (− 6 mm/year). Conversely, a notable uplift was observed in the central city district (13 mm/year) and Xinzheng Airport (12 mm/year). The accuracy assessment of in-situ measurements (GNSS and levelling) yielded an overall root-mean-square error (RMSE) of 2.2 mm/year and an R-square of 0.948. Subsequently, the feature evaluation results based on the XGBoost method suggest that road density and precipitation are the dominant factors affecting land deformation in the entire study area or in the subsidence and uplift zones individually. Nevertheless, the other five factors (groundwater storage, soil type, soil thickness, NDVI, and slope) also act on land deformation individually and are intricately intertwined with each other. Furthermore, hydrogeological analysis from six groundwater wells reveals a synchronous relationship between groundwater level decline and land subsidence. The building load analysis shows a significant correlation between build-up density and subsidence rates, especially for those severe subsidence areas, with the maximum correlation coefficient reaching 0.6312. Finally, the geographic patterns analysis of post-event demonstrated a northeastward trend in land deformation, with a gradual reduction of deformation impact from 2018 to 2022.https://doi.org/10.1038/s41598-025-87789-9Land subsidenceMT-InSARXGBoostHydrogeological analysisBuilt-up index |
| spellingShingle | Zheng Zhou Jiyuan Hu Jiayao Wang Lijun Wang Tianrong Qiao Zhen Li Shiyuan Cheng Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis Scientific Reports Land subsidence MT-InSAR XGBoost Hydrogeological analysis Built-up index |
| title | Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis |
| title_full | Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis |
| title_fullStr | Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis |
| title_full_unstemmed | Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis |
| title_short | Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis |
| title_sort | identifying spatiotemporal pattern and trend prediction of land subsidence in zhengzhou combining mt insar xgboost and hydrogeological analysis |
| topic | Land subsidence MT-InSAR XGBoost Hydrogeological analysis Built-up index |
| url | https://doi.org/10.1038/s41598-025-87789-9 |
| work_keys_str_mv | AT zhengzhou identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT jiyuanhu identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT jiayaowang identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT lijunwang identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT tianrongqiao identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT zhenli identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis AT shiyuancheng identifyingspatiotemporalpatternandtrendpredictionoflandsubsidenceinzhengzhoucombiningmtinsarxgboostandhydrogeologicalanalysis |