Geomorfologit

Geomorfologit is a term used in some scholarly contexts to describe a modeling approach that integrates geomorphology with probabilistic binary outcome analysis. It refers to applying logistic regression and related statistical methods to predict the presence or absence of geomorphic features and processes from environmental covariates.

Etymology and origin: The name blends geomorphology with logit, the logistic function used in statistics. The concept has appeared in regional or project-based studies during the late 20th and early 21st centuries as researchers sought quantitative, probabilistic assessments of landform distribution and change.

Methods: Practitioners assemble geographic and environmental data from field surveys, remote sensing, and digital elevation models. They select predictor variables such as slope, curvature, precipitation, soil type, vegetation, and lithology, and model a binary outcome: feature present or not. Standard logistic regression is common, with extensions to address spatial autocorrelation, non-linear effects, or mixed effects. The approach is often integrated into geographic information systems to produce probability maps.

Applications: Geomorfologit is used to map landslide susceptibility, predict river incision zones, identify potential zones of aggradation or erosion, and support watershed management, hazard planning, and landscape evolution studies. It allows researchers to compare competing hypotheses about drivers of geomorphic change in a probabilistic framework.

Advantages and limitations: The approach yields interpretable probabilities and can be easily incorporated into GIS workflows. Limitations include data quality requirements, sensitivity to spatial autocorrelation and scale, and the risk of oversimplifying complex processes that are inherently dynamic and non-stationary.

See also: geomorphology, logistic regression, spatial statistics, landform mapping.