Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning
Xianmin Wang1,2, Xinlong Zhang1, Jia Bi1
1Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.
International Journal of Environmental Research and Public Health
|November 11, 2022
Summary
This study introduces new criteria for identifying potential landslides, improving accuracy and reducing false alarms. By combining known and potential landslide data, it enhances landslide susceptibility evaluation for better disaster prevention.
Area of Science:
- Earth Science
- Geology
- Remote Sensing
Background:
- Catastrophic landslides are increasing globally due to extreme weather and human activities.
- Existing landslide susceptibility evaluation (LSE) methods often overlook potential landslides, limiting accuracy.
- Current identification of potential landslides neglects crucial disaster-inducing mechanisms, leading to low accuracy and high false alarms.
Purpose of the Study:
- To develop novel synthetic criteria for potential landslide identification, integrating surface deformation, controlling features, and triggering mechanisms.
- To improve the precision and rationality of LSE by combining known and newly identified potential landslides.
- To assess landslide susceptibility in Chaya County using advanced techniques and multisource data.
Main Methods:
- Developed synthetic criteria for potential landslide identification incorporating surface deformation, disaster-controlling, and disaster-triggering characteristics.
- Employed time-series Interferometric Synthetic Aperture Radar (InSAR) and the XGBoost algorithm for landslide susceptibility evaluation.
- Utilized multisource data including geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data.
Main Results:
- Achieved high precision in LSE with AUC of 0.996, Accuracy at 97.98%, TPR of 98.77%, F1-score of 0.98, and Kappa coefficient of 0.96.
- Newly discovered 16 potential landslides, significantly enhancing the understanding of landslide threats.
- Illuminated the development characteristics of potential landslides and the causes of high landslide susceptibility in the study area.
Conclusions:
- The proposed synthetic criteria effectively improve potential landslide recognition accuracy and reduce false alarms.
- Integrating known and potential landslides enhances the precision and rationality of LSE maps.
- The methodology is transferable to other landslide-prone regions globally for improved disaster risk management.
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