Summary

In this chapter, we have learned how to model landslide data to predict the likelihood of landslides at different locations and also to produce a landslide susceptibility map. We used QGIS mainly for data preprocessing, and R for modeling. We have used a very simple model and considered only elevation and slope as explanatory variables for landslide. But, as we discussed at the start of this chapter, landslides can depend upon a number of other factors, such as human settlement, vegetation, settlement, tribal agricultural systems (Jhum, for Bangladesh), proximity to a drainage system, soil type, and so on. But, using the same technique as the one shown here, you can build a more inclusive model. Furthermore, we have considered only the logistic regression model here; using the same data preprocessing technique, you can now fit more sophisticated models, including neural...