Ensemble learning for landslide susceptibility mapping: a review of machine learning and hybrid approaches

Key Points

• Mapping landslide susceptibility using ensemble learning enhances predictive accuracy, offering robust models for diverse terrains.
• Key findings emphasize various machine learning techniques, including decision trees and neural networks, for susceptibility assessment.
• Review assesses hybrid approaches combining different algorithms to improve mapping outcomes and reduce errors in predicting landslides.
• Study underscores the need for region-specific evaluations to refine models, ensuring effective application in real-world scenarios.