Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province
The assessment of geological disaster susceptibility is crucial for early warning, prevention, and mitigation of disasters. Current methods for evaluating geological disasters rely primarily on static factors that cause disasters and historical data at disaster sites or dynamic factors that are intr...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004874 |
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| author | Rongwei Li Pengwei Wang Shucheng Tan Yangbiao Zhou Lifeng Liu Chaodong Gou Yalan Yu |
| author_facet | Rongwei Li Pengwei Wang Shucheng Tan Yangbiao Zhou Lifeng Liu Chaodong Gou Yalan Yu |
| author_sort | Rongwei Li |
| collection | DOAJ |
| description | The assessment of geological disaster susceptibility is crucial for early warning, prevention, and mitigation of disasters. Current methods for evaluating geological disasters rely primarily on static factors that cause disasters and historical data at disaster sites or dynamic factors that are introduced into the evaluation model; however, relatively few studies have focused on the use of dynamic data corrections on the basis of susceptibility evaluation. Interferometric synthetic aperture radar (InSAR) technology can effectively capture surface deformation features. Therefore, this study introduces a correction framework based on small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) data to improve the accuracy of geohazard susceptibility assessment results. This correction framework corrects the susceptibility results of the Random Forest (RF) model, which is based on 12 static factors and historical hazard data, using surface deformation data measured by the SBAS-InSAR technique. Compared with the assessments based on static factors via the RF model, the revised results indicated a reduction in the low susceptibility area by 282.30 km2 and increases in the medium, high, and extremely high susceptibility areas by 90.00 km2, 138.98 km2, and 53.34 km2, respectively. The revised evaluation also revealed a lower density of disaster sites in the low- to high-susceptibility zones than before the revision but a higher density in the extremely high-susceptibility zones. This suggests a greater concentration of disaster sites in areas of higher susceptibility, aligning the zoning results more closely with the actual distribution of the sites. Thus, the proposed method has important reference value for improving the accuracy of regional geological disaster susceptibility evaluation. |
| format | Article |
| id | doaj-art-022126eb427f4ec59da4a0117cb862ee |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-022126eb427f4ec59da4a0117cb862ee2025-01-19T06:24:37ZengElsevierEcological Informatics1574-95412025-03-0185102945Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu ProvinceRongwei Li0Pengwei Wang1Shucheng Tan2Yangbiao Zhou3Lifeng Liu4Chaodong Gou5Yalan Yu6Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, Yunnan, China; Yunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, Yunnan, China; Yunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, ChinaYunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, China; School of Earth Science, Yunnan University, Kunming 650500, Yunnan, China; Corresponding author at: Yunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, China.Yunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, China; School of Earth Science, Yunnan University, Kunming 650500, Yunnan, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, Yunnan, China; Yunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, ChinaYunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, China; School of Earth Science, Yunnan University, Kunming 650500, Yunnan, ChinaYunnan International Joint Laboratory of Critical Mineral Resources, Kunming 650500, Yunnan, China; School of Earth Science, Yunnan University, Kunming 650500, Yunnan, ChinaThe assessment of geological disaster susceptibility is crucial for early warning, prevention, and mitigation of disasters. Current methods for evaluating geological disasters rely primarily on static factors that cause disasters and historical data at disaster sites or dynamic factors that are introduced into the evaluation model; however, relatively few studies have focused on the use of dynamic data corrections on the basis of susceptibility evaluation. Interferometric synthetic aperture radar (InSAR) technology can effectively capture surface deformation features. Therefore, this study introduces a correction framework based on small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) data to improve the accuracy of geohazard susceptibility assessment results. This correction framework corrects the susceptibility results of the Random Forest (RF) model, which is based on 12 static factors and historical hazard data, using surface deformation data measured by the SBAS-InSAR technique. Compared with the assessments based on static factors via the RF model, the revised results indicated a reduction in the low susceptibility area by 282.30 km2 and increases in the medium, high, and extremely high susceptibility areas by 90.00 km2, 138.98 km2, and 53.34 km2, respectively. The revised evaluation also revealed a lower density of disaster sites in the low- to high-susceptibility zones than before the revision but a higher density in the extremely high-susceptibility zones. This suggests a greater concentration of disaster sites in areas of higher susceptibility, aligning the zoning results more closely with the actual distribution of the sites. Thus, the proposed method has important reference value for improving the accuracy of regional geological disaster susceptibility evaluation.http://www.sciencedirect.com/science/article/pii/S1574954124004874Geological disasterSusceptibilitySBAS-InSARKang County |
| spellingShingle | Rongwei Li Pengwei Wang Shucheng Tan Yangbiao Zhou Lifeng Liu Chaodong Gou Yalan Yu Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province Ecological Informatics Geological disaster Susceptibility SBAS-InSAR Kang County |
| title | Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province |
| title_full | Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province |
| title_fullStr | Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province |
| title_full_unstemmed | Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province |
| title_short | Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of SBAS-InSAR technology: A case study of Kang County in Gansu Province |
| title_sort | exploration of slope type geological hazard susceptibility evaluation based on dynamic correction of sbas insar technology a case study of kang county in gansu province |
| topic | Geological disaster Susceptibility SBAS-InSAR Kang County |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004874 |
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