Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China
Land subsidence is a widespread geo-hazard, and it can be effectively monitored with the Interferometric Synthetic Aperture Radar (InSAR) technique. Assessing land subsidence plays a significant role in ensuring safety and enhancing disaster prevention. It requires not only focusing on the extent or...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10816455/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590420181254144 |
|---|---|
| author | Chen Chen Mimi Peng Mahdi Motagh Xinxin Guo Mengdao Xing Yinghui Quan |
| author_facet | Chen Chen Mimi Peng Mahdi Motagh Xinxin Guo Mengdao Xing Yinghui Quan |
| author_sort | Chen Chen |
| collection | DOAJ |
| description | Land subsidence is a widespread geo-hazard, and it can be effectively monitored with the Interferometric Synthetic Aperture Radar (InSAR) technique. Assessing land subsidence plays a significant role in ensuring safety and enhancing disaster prevention. It requires not only focusing on the extent or rate of deformation but also evaluating the susceptibility and risk of subsidence. In this article, we propose a new comprehensive subsidence susceptibility and risk assessment strategy by integrating InSAR observation and hybrid machine learning models, which is first and successfully employed over Xi'an area. In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (Root Mean Square Error = 3.53 mm) and susceptibility (Area Under the Curve = 0.97). Also, the authenticity and reliability of the susceptibility results from RF model are improved by further Isotonic regression calibration processes. Then, subsidence risk map is obtained from the hazard and vulnerability assessment using the Analytic Hierarchy Process by combining multiple conditional factors. Remarkably, the results revealed that regions with the very high and high risk level of subsidence are 12.33 <inline-formula><tex-math notation="LaTeX">$\text{km}^{2}$</tex-math></inline-formula> and correspond to 1.34<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the total. It further found that the groundwater level and its changes are the domain factors in Xi'an from machine learning method. This work provides an integrated assessment approach for subsidence from a new perspective, and the findings can serve as theoretical support for early warning and disaster prevention. |
| format | Article |
| id | doaj-art-ba306953d1c84f5098804ff9af7a892b |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ba306953d1c84f5098804ff9af7a892b2025-01-24T00:00:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183625363910.1109/JSTARS.2024.352299510816455Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, ChinaChen Chen0Mimi Peng1https://orcid.org/0000-0002-4521-2109Mahdi Motagh2https://orcid.org/0000-0001-7434-3696Xinxin Guo3Mengdao Xing4https://orcid.org/0000-0002-4084-0915Yinghui Quan5https://orcid.org/0000-0001-6541-9441Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xi'an Key Laboratory of Advanced Remote Sensing, Xidian University, Xi'an, ChinaKey Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xi'an Key Laboratory of Advanced Remote Sensing, Xidian University, Xi'an, ChinaHelmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing and Geoinformatics, Potsdam, GermanySchool of Geological Engineering and Geomatics, Chang'an University, Xi'an, ChinaAcademy of Advanced Interdisciplinary Research, Xidian University, Xi'an, ChinaKey Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xi'an Key Laboratory of Advanced Remote Sensing, Xidian University, Xi'an, ChinaLand subsidence is a widespread geo-hazard, and it can be effectively monitored with the Interferometric Synthetic Aperture Radar (InSAR) technique. Assessing land subsidence plays a significant role in ensuring safety and enhancing disaster prevention. It requires not only focusing on the extent or rate of deformation but also evaluating the susceptibility and risk of subsidence. In this article, we propose a new comprehensive subsidence susceptibility and risk assessment strategy by integrating InSAR observation and hybrid machine learning models, which is first and successfully employed over Xi'an area. In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (Root Mean Square Error = 3.53 mm) and susceptibility (Area Under the Curve = 0.97). Also, the authenticity and reliability of the susceptibility results from RF model are improved by further Isotonic regression calibration processes. Then, subsidence risk map is obtained from the hazard and vulnerability assessment using the Analytic Hierarchy Process by combining multiple conditional factors. Remarkably, the results revealed that regions with the very high and high risk level of subsidence are 12.33 <inline-formula><tex-math notation="LaTeX">$\text{km}^{2}$</tex-math></inline-formula> and correspond to 1.34<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the total. It further found that the groundwater level and its changes are the domain factors in Xi'an from machine learning method. This work provides an integrated assessment approach for subsidence from a new perspective, and the findings can serve as theoretical support for early warning and disaster prevention.https://ieeexplore.ieee.org/document/10816455/Interferometric Synthetic Aperture Radar (InSAR)land subsidencemachine learningsusceptibility and risk mapping |
| spellingShingle | Chen Chen Mimi Peng Mahdi Motagh Xinxin Guo Mengdao Xing Yinghui Quan Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Interferometric Synthetic Aperture Radar (InSAR) land subsidence machine learning susceptibility and risk mapping |
| title | Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China |
| title_full | Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China |
| title_fullStr | Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China |
| title_full_unstemmed | Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China |
| title_short | Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China |
| title_sort | mapping susceptibility and risk of land subsidence by integrating insar and hybrid machine learning models a case study in xi x0027 an china |
| topic | Interferometric Synthetic Aperture Radar (InSAR) land subsidence machine learning susceptibility and risk mapping |
| url | https://ieeexplore.ieee.org/document/10816455/ |
| work_keys_str_mv | AT chenchen mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina AT mimipeng mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina AT mahdimotagh mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina AT xinxinguo mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina AT mengdaoxing mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina AT yinghuiquan mappingsusceptibilityandriskoflandsubsidencebyintegratinginsarandhybridmachinelearningmodelsacasestudyinxix0027anchina |