Susceptibility evaluation and mapping of China’s landslides based on multi-source data
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.
KeywordsLandslide susceptibility Empirical model Historical landslide events ANN Hot spots
The work described in this paper is funded by National Basic Research Program of China (Project No. 2013CB733204), National Natural Science Foundation of China (No.41201380) and Key Laboratory of Advanced Engineering Surveying of NASMG (TJES1010), and is also supported by the Center of Spatial Information Science and Sustainable Development Applications, Tongji University.
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