Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway
The red bed strata region is characterized by distinct interbedded soft and hard water–rock properties and significant water sensitivity, resulting in the frequent occurrence of landslide disasters. Despite the widespread application of Interferometric Synthetic Aperture Radar (InSAR) technology in...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576553302622208 |
|---|---|
| author | Yifan Cao Zhifang Zhao Mingchun Wen Xin Zhao Dingyi Zhou Jingyi Qin Liu Ouyang Jingyao Cao |
| author_facet | Yifan Cao Zhifang Zhao Mingchun Wen Xin Zhao Dingyi Zhou Jingyi Qin Liu Ouyang Jingyao Cao |
| author_sort | Yifan Cao |
| collection | DOAJ |
| description | The red bed strata region is characterized by distinct interbedded soft and hard water–rock properties and significant water sensitivity, resulting in the frequent occurrence of landslide disasters. Despite the widespread application of Interferometric Synthetic Aperture Radar (InSAR) technology in landslide identification, challenges such as low recognition rates and difficulties in objective assessment continue to persist. This study focuses on a section of the Nanjing Expressway in the western part of Yunnan Province as the research area and utilizes Small Baseline Subset InSAR (SBAS-InSAR) technology in conjunction with optical imagery to identify landslide disaster points. By analyzing nine evaluation indicators, this study assesses the susceptibility of landslide disasters in the research area by applying Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Stacking Ensemble Strategies (Stacking). Furthermore, Receiver Operating Characteristic (ROC) curves are used to evaluate the accuracy of the models and analyze the relative importance of each evaluation factor. The results indicate that: (1) the analysis of 245 datasets of ascending and descending orbits from 2017 to 2022 yielded deformation rates, with maximum positive and negative deformation rates of 37.02 mm/yr and −46.47 mm/yr, respectively. In combination with optical imagery data, a total of 521 landslide disaster points were identified. (2) In comparison to individual machine learning models, the Stacking demonstrated superior performance, with prediction capabilities and accuracy that surpassed other models. The Area Under the Curve (AUC) values increased by 2.55 %, 2.82 %, and 5.39 % compared to RF, XGBoost, and CatBoost, respectively. The findings from the stacking reveal that high-risk areas comprise 12.29 % of the total area of the research zone, predominantly located along the northern highway, where average annual rainfall and topographic relief are the primary driving factors for landslide occurrences. |
| format | Article |
| id | doaj-art-79615a4880f34cd880de332494e63738 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-79615a4880f34cd880de332494e637382025-01-31T05:10:26ZengElsevierEcological Indicators1470-160X2025-01-01170113002Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong ExpresswayYifan Cao0Zhifang Zhao1Mingchun Wen2Xin Zhao3Dingyi Zhou4Jingyi Qin5Liu Ouyang6Jingyao Cao7Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, China; Corresponding author.Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaSchool of Public Security and Emergency Management, Kunming University of Science and Technology, Kunming 650093, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, China; Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China; Research Center of Domestic High-resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China; Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, ChinaThe red bed strata region is characterized by distinct interbedded soft and hard water–rock properties and significant water sensitivity, resulting in the frequent occurrence of landslide disasters. Despite the widespread application of Interferometric Synthetic Aperture Radar (InSAR) technology in landslide identification, challenges such as low recognition rates and difficulties in objective assessment continue to persist. This study focuses on a section of the Nanjing Expressway in the western part of Yunnan Province as the research area and utilizes Small Baseline Subset InSAR (SBAS-InSAR) technology in conjunction with optical imagery to identify landslide disaster points. By analyzing nine evaluation indicators, this study assesses the susceptibility of landslide disasters in the research area by applying Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Stacking Ensemble Strategies (Stacking). Furthermore, Receiver Operating Characteristic (ROC) curves are used to evaluate the accuracy of the models and analyze the relative importance of each evaluation factor. The results indicate that: (1) the analysis of 245 datasets of ascending and descending orbits from 2017 to 2022 yielded deformation rates, with maximum positive and negative deformation rates of 37.02 mm/yr and −46.47 mm/yr, respectively. In combination with optical imagery data, a total of 521 landslide disaster points were identified. (2) In comparison to individual machine learning models, the Stacking demonstrated superior performance, with prediction capabilities and accuracy that surpassed other models. The Area Under the Curve (AUC) values increased by 2.55 %, 2.82 %, and 5.39 % compared to RF, XGBoost, and CatBoost, respectively. The findings from the stacking reveal that high-risk areas comprise 12.29 % of the total area of the research zone, predominantly located along the northern highway, where average annual rainfall and topographic relief are the primary driving factors for landslide occurrences.http://www.sciencedirect.com/science/article/pii/S1470160X24014596LandslideSBAS-InSARRFXGBoostCatBoostStacking |
| spellingShingle | Yifan Cao Zhifang Zhao Mingchun Wen Xin Zhao Dingyi Zhou Jingyi Qin Liu Ouyang Jingyao Cao Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway Ecological Indicators Landslide SBAS-InSAR RF XGBoost CatBoost Stacking |
| title | Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway |
| title_full | Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway |
| title_fullStr | Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway |
| title_full_unstemmed | Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway |
| title_short | Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway |
| title_sort | identification and susceptibility assessment of landslide disasters in the red bed formation along the nanjian jingdong expressway |
| topic | Landslide SBAS-InSAR RF XGBoost CatBoost Stacking |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014596 |
| work_keys_str_mv | AT yifancao identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT zhifangzhao identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT mingchunwen identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT xinzhao identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT dingyizhou identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT jingyiqin identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT liuouyang identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway AT jingyaocao identificationandsusceptibilityassessmentoflandslidedisastersintheredbedformationalongthenanjianjingdongexpressway |