Landslide Prediction Methods: A Comparative Overview of Traditional and AI-Based Decision-Making Approaches

Abstract

Landslides pose significant global hazards, and traditional methods for risk assessment, such as expert systems and geotechnical models, are resource-intensive and limited by human subjectivity. With the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods have become prominent in landslide detection, offering advanced capabilities for modelling complex geotechnical data. Despite initial reliance on visual interpretation of remote sensing data, AI-driven methods have shown potential to improve accuracy and objectivity. ML models have been used to detect landslides based on pixel data and geological features, while DL models, especially CNNs like U-net and ResNet, have gradually replaced ML. However, DL methods are still in their early stages and face challenges due to the complexity of landslide characteristics across regions.