Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake
Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the expl...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2366362 |
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| author | Shiqian Yin Zebing Dai Ying Zeng |
| author_facet | Shiqian Yin Zebing Dai Ying Zeng |
| author_sort | Shiqian Yin |
| collection | DOAJ |
| description | Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation. |
| format | Article |
| id | doaj-art-28f7311b8bdf433bbf9e4ba3094af7b5 |
| institution | OA Journals |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-28f7311b8bdf433bbf9e4ba3094af7b52025-08-20T01:59:04ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2366362Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquakeShiqian Yin0Zebing Dai1Ying Zeng2Yunnan Institute of Geological Science, Kunming, ChinaYunnan Institute of Geological Science, Kunming, ChinaSouthwest Jiaotong University, Chengdu, ChinaEarthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.https://www.tandfonline.com/doi/10.1080/19475705.2024.2366362SBAS-InSARearthquake-induced landslideslandslide susceptibility predictionmachine learningremote sensing |
| spellingShingle | Shiqian Yin Zebing Dai Ying Zeng Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake Geomatics, Natural Hazards & Risk SBAS-InSAR earthquake-induced landslides landslide susceptibility prediction machine learning remote sensing |
| title | Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake |
| title_full | Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake |
| title_fullStr | Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake |
| title_full_unstemmed | Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake |
| title_short | Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake |
| title_sort | optimized landslide susceptibility prediction based on sbas insar case study of the jiuzhaigou ms7 0 earthquake |
| topic | SBAS-InSAR earthquake-induced landslides landslide susceptibility prediction machine learning remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2366362 |
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