This document discusses using machine learning to identify susceptible landslide areas. It describes applying the weight of evidence method to obtain susceptible areas using factors like geological conditions, terrain, and rainfall. Random forest classification with decision trees was used to analyze data from ArcGIS and R software on landslide locations from Google Earth and field surveys. The trained model calculated susceptibility for buildings in settlements and found landslide occurrence has increased in the Himalayas region over the last four decades, with high rainfall being a major triggering factor.

1. Identification of Susceptible landslide
areas using Machine Learning
Author:
Aman Kumar & Chirag Khandelwal

2. Abstract
Landslide encloses all varieties of mass movements consisting of
different types of rock fall, topple and debris flow.These are short lived
and suddenly occurring phenomena that causes considerable loss of life,
widespread damages to infrastructure and properties. Landslide
susceptibility mapping is very important tool to identify potential or
more effective landslide-prone areas. Here, the weight of evidence
method is applied to obtain the susceptible landslide areas.Weight of
evidence model is commonly applied to the landslide study as it is
widely acceptable and easy to use. So here we have used here to make it
more effective identification.

3. Causes of Landslide:
Landslide susceptible classification basically depends on different
physical as well as climatic factors:
• Local geological condition with high elevation difference, rugged terrain and
loose rock and soil
• Natural disasters (such as landslide, collapse, glacial lake outburst, etc.) often
interact with rivers, causing river dimension and landslide dam
• poor engineered road construction and the settlement planning is not in place
• The settlement distribution is concentrated in the mountain plane

4. Processing
Techniques:
• ArcGIS and R software were used to process and analyze
the data to generate model for landslide susceptibility.
• The landslide data were collected from Google Earth pro
and field survey, which was completed in early August
2019.
• Random forest classification were composed of multiple
decision tress.
• The trained model was used to calculate the landslide
susceptibility of each buildings in settlements.

5. Conclusion:
The occurrence of landslides in Himalayas is a frequent and
widespread activity.
Slope failure activities show increasing trends and there is
not only upward rise in annual and decadal frequency of
landslides but the number of years with exceptionally high
occurrence during which decade has also been increased.
Landslide occurrence during last four decades has been very
high in these regions.
The high intensity of rainfall, particularly in monsoons, is one
of the major triggering factors for such incidents.