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Susceptibility evaluation and mapping of China’s landslides based on multi-source data

Author

Listed:
  • Chun Liu
  • Weiyue Li
  • Hangbin Wu
  • Ping Lu
  • Kai Sang
  • Weiwei Sun
  • Wen Chen
  • Yang Hong
  • Rongxing Li

Abstract

Landslides are occurring more frequently in China under the conditions of extreme rainfall and changing climate, according to News reports. Landslide hazard assessment remains an international focus on disaster prevention and mitigation, and it is an important step for compiling and quantitatively characterizing landslide damages. This paper collected and analyzed the historical landslide events data of the past 60 years in China. Validated by the frequencies and distributions of landslides, nine key factors (lithology, convexity, slope gradient, slope aspect, elevation, soil property, vegetation coverage, flow, and fracture) are selected to construct landslide susceptibility (LS) empirical models by back-propagation artificial neural network method. By integrating landslide empirical models with surface multi-source geospatial and remote sensing data, this paper further performs a large-scale LS assessment throughout China. The resulting landslide hazard assessment map of China clearly illustrates the hot spots of the high landslide potential areas, mostly concentrated in the southwest. The study implements a complete framework of multi-source data collecting, processing, modeling, and synthesizing that fulfills the assessment of LS and provides a theoretical basis and practical guide for predicting and mitigating landslide disasters potentially throughout China. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Chun Liu & Weiyue Li & Hangbin Wu & Ping Lu & Kai Sang & Weiwei Sun & Wen Chen & Yang Hong & Rongxing Li, 2013. "Susceptibility evaluation and mapping of China’s landslides based on multi-source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(3), pages 1477-1495, December.
    • Handle: RePEc:spr:nathaz:v:69:y:2013:i:3:p:1477-1495
    DOI: 10.1007/s11069-013-0759-y
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    References listed on IDEAS

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    1. Lenneal J. Henderson, 2004. "Emergency and Disaster: Pervasive Risk and Public Bureaucracy in Developing Nations," Public Organization Review, Springer, vol. 4(2), pages 103-119, June.
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    Citations

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    Cited by:

    1. Thomas Stanley & Dalia B. Kirschbaum, 2017. "A heuristic approach to global landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 145-164, May.
    2. Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1393-1426, November.
    3. George Gaprindashvili & Cees Westen, 2016. "Generation of a national landslide hazard and risk map for the country of Georgia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(1), pages 69-101, January.
    4. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 173-204, January.
    5. Hamid Reza Pourghasemi & Amiya Gayen & Sungjae Park & Chang-Wook Lee & Saro Lee, 2018. "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms," Sustainability, MDPI, vol. 10(10), pages 1-23, October.

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