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	Landslide Susceptibility Mapping	Liquefaction Hazard Assessment
Field	Disaster Studies	Disaster Studies
Family	Process / pipeline	Process / pipeline
Year of origin	2006	2017
Originator	Fausto Guzzetti, Paola Reichenbach and colleagues (CNR-IRPI; statistical landslide susceptibility tradition)	Jing Zhu, Laurie Baise & Eric Thompson (geospatial model); engineering-geology liquefaction-zonation tradition
Type	Spatial statistical classification pipeline over mapping units	Spatial hazard-mapping pipeline combining susceptibility, demand and probability
Seminal source	Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60-91.	Zhu, J., Baise, L. G., & Thompson, E. M. (2017). An Updated Geospatial Liquefaction Model for Global Application. Bulletin of the Seismological Society of America, 107(3), 1365-1385.
Aliases	Landslide Susceptibility Modeling, Slope-Failure Susceptibility Mapping, Statistical Landslide Hazard Mapping, Landslide Probability Mapping	Liquefaction Hazard Mapping, Regional Liquefaction Susceptibility Mapping, Geospatial Liquefaction Modeling, Liquefaction Potential Zonation
Related	3	3
Summary	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.	Liquefaction hazard assessment maps where earthquake-induced liquefaction is likely to occur and how severe its surface effects will be, across areas ranging from a city to a whole region. Unlike site-specific triggering analysis, which evaluates a single soil column from borehole data, regional assessment must predict liquefaction over wide areas where detailed subsurface data are sparse, so it relies on geospatial proxies for soil susceptibility together with a map of seismic demand. Zhu, Baise, and Thompson's 2017 geospatial model exemplifies the modern approach, predicting the probability of liquefaction from globally available variables such as slope-derived shear-wave velocity, a compound topographic index, and magnitude-adjusted peak ground acceleration, calibrated on documented liquefaction from past earthquakes. The Youd and Idriss 2001 consensus framework supplies the underlying site-scale physics and the severity indices that translate probability into expected damage. The product is a hazard map showing the spatial probability and intensity of liquefaction. It supports rapid post-earthquake response, loss estimation, and land-use planning where borehole-by-borehole analysis is infeasible.