A Network and Delphi Analysis Approach to Identify Steep Slope Risk Factors: South Korean Case Study

초록

Rapid urbanization in mountainous regions increases the risk of slope collapse, highlighting the need for a robust risk assessment framework. In South Korea, existing models struggle with environmental dynamics and weather irregularities, and local government agencies are slow to respond to emerging challenges. This study addresses these issues using a three-step process. First, key risk factors—such as slope elevation, soil type, 1-hour disaster rainfall, snow accumulation, and forest fires—were identified using text analytics with Gephi and KH Coder. These factors were then validated through a Delphi analysis with 20 experts, achieving consensus on their significance. Finally, the validated factors were tested on 200 steep slopes to assess their practical applicability, leading to their integration into current guidelines. The results showed improved sensitivity and effectiveness in risk assessments. This study offers essential insights for engineers and policymakers, providing a model that can enhance risk management practices, prioritize mitigation measures, and strengthen community resilience in mountainous regions globally.

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