Landslide Susceptibility Mapping Methods—A Review

Landslides are a prevalent geological hazard in many regions globally and have a profound effect on the resources and social fabric of the affected community. Identification of landslide-prone zones is mandatory for developing land use plans in regions affected by landslides. Landslide susceptibility maps are crucial for mapping landslide hazards and evaluating the risk resulting from landslides. Zones susceptible to the geologic phenomenon of landslides are identified and mapped using various methods and models. Off-late the increase in frequency of the landslide hazards in many hill and mountain regions around the globe mandates landslide susceptibility mapping. The choice of the method depends on the factors and data available. Common methods include deterministic modelling for specific slopes and statistical & heuristic models for mapping landslide susceptibility at regional scale. Statistical and probabilistic methods like frequency ratio, conditional probability, certainty factor and logistic regression, etc. are popularly used for mapping susceptibility. Off-late machine learning and deep mining techniques are also widely adopted for assessing susceptibility. Heuristic models like analytical hierarchical and analytical network process are also common. The choice of both the model to map susceptibility and factors used to assess the susceptibility of the region are dictated by factors like the local geo-environmental set-up, quality of data used and its reliability. This chapter explores the suitability of various susceptibility models, their merits and limitations in different geo-environmental scenarios. Also, a detailed review on the various topographic, hydrologic, geological, geotechnical, environmental and anthropogenic factors is presented arguing their effectiveness in specific geologic and environmental regional set-up.