The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

It is shown in this paper how the emergence of fuzzy set theory and the theory of monotone
measures considerably expanded the framework for formalizing uncertainty and suggested …

Flash floods are one of the most devastating natural hazards; they occur within a catchment
(region) where the response time of the drainage basin is short. Identification of probable …

The concept of leveraging the predictive capacity of predisposing factors for landslide
susceptibility (LS) modeling has been continuously improved in recent work focusing on …

This presents a range of theories about uncertainty, all of them mathematical and allowing
quantitative treatment. A definition of uncertainty is automatically associated with one of …

In many areas of science and engineering, we have a class (“population”) of objects, and we
are interested in the values of one or several quantities characterizing objects from this …

The representation and quantification of uncertainty has received increasing attention in
machine learning in the recent past. The formalism of credal sets provides an interesting …

Adequate uncertainty representation and quantification have become imperative in various
scientific disciplines, especially in machine learning and artificial intelligence. As an …

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments
are useful to mitigate human casualties, loss of property, and damage to natural resources …

The theory of Evidence, or Shafer-Dempster theory (DST), has been widely used in
applications. The DST is based on the concept of a basic probability assignment. An …