Comprehensive Methodology for Landslide Risk Assessment Using Fuzzy Logic Systems: A Step-by-Step Approach

Abstract

This paper aims to address the limitations of traditional landslide risk assessment methods by proposing an innovative approach using Fuzzy Logic Systems (FLS). Traditional methods often struggle with the inherent uncertainties associated with landslide risks, including data ambiguity and subjective expert opinions. The paper describes a fuzzy inference system designed to manage such uncertainties by offering a mathematical framework that transforms qualitative risk factors into quantitative risk assessments. This framework uses fuzzy set theory and Mamdani-type inference systems to evaluate landslide risk based on two key variables: the probability of landsliding and the potential adverse consequences. The system employs membership functions to fuzzify these variables and produces a comprehensive risk severity that captures the intricacies of real-world risk factors.