Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Xianmin Wang (), Xinlong Zhang, Jia Bi, Xudong Zhang, Shiqiang Deng, Zhiwei Liu, Lizhe Wang and Haixiang Guo
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Xianmin Wang: Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Xinlong Zhang: Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Jia Bi: Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Xudong Zhang: Institute of Geological Survey of Tibet Autonomous Region, Lhasa 850000, China
Shiqiang Deng: The Fifth Geological Brigade, Bureau of Geology and Mineral Exploration and Development of Tibet Autonomous Region, Glomud 816000, China
Zhiwei Liu: Hubei Subsurface Multi-Scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Lizhe Wang: Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China
Haixiang Guo: Laboratory of Natural Disaster Risk Prevention and Emergency Management, School of Economics and Management, China University of Geosciences, Wuhan 430074, China

IJERPH, 2022, vol. 19, issue 21, 1-26

Abstract: Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Keywords: landslide susceptibility evaluation; hidden landslide; remote sensing; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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