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Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning
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Ying JIA1, Caiyan WU1, *, Lijuan WANG2, Xinhan YING1, Qi MENG1, Yi YUAN3, Jun LIAO2, Shiqian MA1
Journal of Geodesy and Geodynamics | 2025, 45(3) : 231 - 238
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Journal of Geodesy and Geodynamics | 2025, 45(3): 231-238
Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning
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Ying JIA1, Caiyan WU1, *, Lijuan WANG2, Xinhan YING1, Qi MENG1, Yi YUAN3, Jun LIAO2, Shiqian MA1
Affiliations
  • 1 School of Environment and Resource, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China
  • 2 Sichuan Academy of Safety Science and Technology, 18 Fourth West-Wuke Road, Chengdu 610045, China
  • 3 School of Civil Engineering and Architecture, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China

    • JIA Ying, postgraduate, majors in the application of 3S technology in geological hazard, E-mail:

About Author:

JIA Ying, postgraduate, majors in the application of 3S technology in geological hazard, E-mail:

Published: 2025-03-15 doi: 10.14075/j.jgg.2024.04.178
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We comprehensively use InSAR and machine learning technology to evaluate landslide susceptibility in Jinyang county, Sichuan province. The data set is updated by interpreting landslides. Based on 12 evaluation factors, we use three models including random forest(RF), support vector machine(SVM) and extreme gradient boosting(XGBoost) for training in Python environment to complete landslide susceptibility mapping. We use ROC curve to verify prediction performance. We optimize the negative samples, obtain the landslide susceptibility evaluation results after sample optimization using machine learning and update the landslide susceptibility results using surface LOS deformation rate. The results show that the three machine learning models have good zoning effect, and the mapping effect of XGBoost model is best. The accuracy of XGBoost model after sample optimization is highest, and the AUC value reaches 0.95. The surface deformation rate obtained by SBAS-InSAR can reduce zoning errors and give timeliness to landslide susceptibility evaluation.

landslide susceptibility evaluation  /  SBAS-InSAR  /  machine learning  /  sample optimization
贾应, 吴彩燕, 王立娟, 应欣翰, 蒙齐, 袁怡, 廖军, 马世乾. 融合InSAR与机器学习的滑坡易发性评价. 大地测量与地球动力学, 2025 , 45 (3) : 231 -238 . DOI: 10.14075/j.jgg.2024.04.178
Ying JIA, Caiyan WU, Lijuan WANG, Xinhan YING, Qi MENG, Yi YUAN, Jun LIAO, Shiqian MA. Landslide Susceptibility Evaluation by Integrating InSAR and Machine Learning[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (3) : 231 -238 . DOI: 10.14075/j.jgg.2024.04.178
Funding
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  • National Natural Science Foundation of China(41301587)
  • Open Fund of Sichuan Key Laboratory of Measurement and Control of Major Hazard Sources(KFKT-2023-01)
Appendix
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Year 2025 volume 45 Issue 3
31
11
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Article Info
doi: 10.14075/j.jgg.2024.04.178
  • Receive Date：2024-04-15
  • Online Date：2026-10-30
  • Published：2025-03-15
Article Data
Affiliations
History
  • Received：2024-04-15
Funding
National Natural Science Foundation of China(41301587)
Open Fund of Sichuan Key Laboratory of Measurement and Control of Major Hazard Sources(KFKT-2023-01)
Affiliations
    1 School of Environment and Resource, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China
    2 Sichuan Academy of Safety Science and Technology, 18 Fourth West-Wuke Road, Chengdu 610045, China
    3 School of Civil Engineering and Architecture, Southwest University of Science and Technology, 59 Mid-Qinglong Road, Mianyang 621010, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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