GIS-based Research On Spatial Simulation Of Landslide In Lanzhou

Landslide is one of typical environment geological hazards, whose harms become more and more serious. There are plenty of uncertainty of the occurrence, location and type of the landslide. Based on the analysis for cause and effect of the geologic factors, we must predict the landslide and grade it so that we can control and manage the slope more efficiently.In this paper, we discuss the two facet of the landslide factor, the first is inner factor and the other is outer factor. Based on the GIS, we extract 15 factors of landslide: lithology, stratum, altitude, slope, aspect, plan curvature, profile curvature, slope length factor, slope location, average rainfall, highest rain intensity in 10 minutes, highest rain intensity in 60 minutes, NDVI, river cost distance and landuse. We divide these factors into two types: qualified factors and normal factors. For qualified factors, we calculate the likelihood ratio between landslide and them, and discuss the relationship between landslide and each factor. Afterward, we classified each factor based on the likelihood ratio.After classified the qualified factors, we calculate the certainty, factor (CF) of each class of each factor, and evaluate the most important factor class. Based on CF, we divide the study area into several regions with different hazard degree, and sort the factor into the different regions. After this, we integrate the different class of one factor into one value according to some rules. So, we get four factors, which have positive response for landslide. In the end, we use multi-variable regression model to build an equation to describe the spatial frequency of the occurrence of landslide.On the other hand, we start with grid data. We use logistic model (before we calculate the model, we select part of the data randomly). According to the result, we get the coefficient, namedβ*. Based on this index, we order the coefficient and sort the different class. Based on the rule of likelihood ratio, we get the likelihood ratio between landslide probability and landslide. And then, we classified the landslide probability and get the landslide hazard distribution map. Finally, we compare the predict precision of the two methods. We find that logistic model is more accurate than multi-variable regression model.Throughout the study, we predict the landslide according to homogeneous unit based on vector data model and grid unit based on grid data model. Based on likelihood ratio classified factor, we use multi-variable regression model and logistic regression model to simulate the hazards of landslide. But the regression model based on homogeneous unit is not as accurate as we expect. The non-linearity characteristic is the most important reason. Secondly, logistic regression model can predict the landslide probability effectively, but it is not as effective as CF in evaluating the relationship between landslide and each factor.