Abstract
Abstract. Under the circumstances of global climate change, nowadays landslide occurs in China more frequently than ever before. The landslide hazard and risk assessment remains an international focus on disaster prevention and mitigation. It is also an important approach for compiling and quantitatively characterizing landslide damages. By integrating empirical models for landslide disasters, and through multi-temporal ground data and remote sensing data, this paper will perform a landslide susceptibility assessment throughout China. A landslide susceptibility (LS) map will then be produced, which can be used for disaster evaluation, and provide basis for analyzing China's major landslide-affected regions. Firstly, based on previous research of landslide susceptibility assessment, this paper collects and analyzes the historical landslide event data (location, quantity and distribution) of past sixty years in China as a reference for late-stage studies. Secondly, this paper will make use of regional GIS data of the whole country provided by the National Geomatics Centre and China Meteorological Administration, including regional precipitation data, and satellite remote sensing data such as from TRMM and MODIS. By referring to historical landslide data of past sixty years, it is possible to develop models for assessing LS, including producing empirical models for prediction, and discovering both static and dynamic key factors, such as topography and landforms (elevation, curvature and slope), geologic conditions (lithology of the strata), soil type, vegetation cover, hydrological conditions (flow distribution). In addition, by analyzing historical data and combining empirical models, it is possible to synthesize a regional statistical model and perform a LS assessment. Finally, based on the 1km×1km grid, the LS map is then produced by ANN learning and multiplying the weighted factor layers. The validation is performed with reference to the frequency and distribution of historical data. This research reveals the spatiotemporal distribution of landslide disasters in China. The study develops a complete algorithm of data collecting, processing, modelling and synthesizing, which fulfils the assessment of landslide susceptibility, and provides theoretical basis for prediction and forecast of landslide disasters throughout China.
Highlights
Natural disasters are abnormal and inevitable phenomena, from the nature on which human beings depend to live
According to the summary from CDSTM, the formation conditions of landslide in China which mainly contained: Jurassic, Mudstone of Cretaceous, Shale, Argillaceous Sandstone, Siltstone, Coal Beds, Sandy Slate, Phyllite; the types of convexity include: concave, flat, protrude; with the increase of slope gradient, the component force of gravity in the slope direction, and landslide would have higher risks (Dai et al, 2002); slope aspect is mainly divided into eight directions; the relationship exists between elevation and landslide, such as in high mountains, which were comprised of weathered and tough rocks, that is less likely for landslide ; the higher the vegetation coverage is, the smaller the landslide possibility; landslide is bound up with the distribution of flow and fracture (Montgomery et al, 2002)
Input layers include the above 9 factors: Lithology, Convexity, Gradient, Aspect, Elevation, Soil property, Vegetation cover, Flow, Fracture; the Artificial neural network (ANN) output may be considered as the measurements of the occurrence of landslide (Figure 1)
Summary
Natural disasters are abnormal and inevitable phenomena, from the nature on which human beings depend to live. It did harm to the human society, mainly including earthquake, volcano, landslide, debris flow, typhoon, flood, soil erosion, desertification, water pollution, and so on. These disasters are complicated and closely connected with environmental degradation and human life (Henderson, 2004). LS assessment is the quantitative or qualitative evaluation for the existing or potential type, volume, distribution of some area’s landslide, and LS mapping would conduce to us the space distribution of one regional slope instability probability (Mathew et al, 2008). We used the ANN black box by capturing the connection weights among various inputs, with multi-temporal ground and remote sensing satellite data for susceptibility evaluation and mapping of China’s landslide disaster
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