Discerning landslide susceptibility using rough sets

Abstract

Rough set theory has been primarily known as a mathematical approach for analysis of a vague description of objects. This paper explores the use of rough set theory to manage the complexity of geographic characteristics of landslide susceptibility and extract rules describing the relationships between landslide conditioning factors and landslide events. The proposed modeling approach is illustrated using a case study of the Clearwater National Forest in central Idaho, which experienced significant and widespread landslide events in recent years. In this approach the landslide susceptibility is derived from decision rules of variable strengths computed in rough set analysis and presented on maps for roaded and roadless areas. The rough set approach to modeling landslide susceptibility offers advantages over other modeling methods in accounting for data vagueness and uncertainty and in potentially reducing data collection needs. From an application perspective the rough set-based approach is promising as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.