We will use logistic regression here as it can be used to model dependent variables with values of 1 (for our case, landslide) and 0 (no landslide):
logistic_fit = glm(as.factor(hazard)~ DEM_PC_UTM + Slope_PC, data=landslide, family=binomial)summary(logistic_fit)
Now, using the estimates of Intercept and coefficients, we calculate the susceptibility. For this case, the value of (Intercept) is 3.111299, the value of DEM_PC_UTM is -0.010680, and the value of Slope_PC is -0.123692. You might find something different for these values.
Now, load the elevation and slope raster:
library(raster)dem = raster("F:/Hands-on Geospatial ...
