Landslide Susceptibility Mapping

Landslide susceptibility mapping estimates where slope failures are likely to occur by statistically relating a mapped inventory of past landslides to the terrain conditions that predispose a slope to fail. The premise, articulated across the statistical landslide literature that Guzzetti, Reichenbach, and colleagues helped systematize, is that landslides recur under geological and morphological conditions similar to those that produced them before, so the spatial pattern of past failures reveals the susceptibility of as-yet unfailed terrain. The analyst partitions the landscape into mapping units, characterizes each by conditioning factors such as slope, aspect, lithology, and land cover, and fits a classifier — logistic regression, discriminant analysis, or machine learning — to predict the probability of failure. Reichenbach and co-authors' 2018 review of 565 studies catalogued the methods, factors, and validation practices, while Guzzetti and co-workers' 2006 paper established how to rigorously assess model quality. The output is a zonation ranking terrain from low to high susceptibility. Susceptibility maps describe spatial likelihood, not when or how large a failure will be.