Landslide susceptibility assessment is a quantitative or semi-quantitative method for estimating the spatial probability of future landslide occurrence, given local terrain and environmental conditions, without explicitly modeling triggering events in time. It typically integrates conditioning factors such as slope gradient, lithology, soil properties, land use, drainage density, and structural features with landslide inventory data using statistical (e.g., logistic regression, weights-of-evidence), machine learning (e.g., random forests, support vector machines), or deterministic approaches (e.g., physically based slope stability models). The output is usually a susceptibility map classifying terrain into relative hazard levels, supporting land-use planning and risk analyses.
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