A review of geological and triggering factors influencing landslide susceptibility: artificial intelligence-based trends in mapping and prediction

Abstract Landslides are one of the most devastating natural hazards in many regions of the globe and lead to thousands of deaths globally each year. Factors affecting landslides vary in different climates. The complex interaction of geological and triggering factors leads to slope failures and difficulty in landslide prediction. Therefore, this study aimed to do a bibliometric analysis and review of geological and triggering factors used in previous studies for landslide susceptibility mapping and prediction. This review includes 102 scientific articles from peer-reviewed Web of Science journals from 2020 to 2024. The review has four components such as (i) research publication trends and their geographic distribution; (ii) analysis of the role of prominent and triggering factors in recent landslides; (iii) integration of geological and triggering factors into the artificial intelligence algorithms; (iv) case studies analysis on the use of AI algorithms for landslide susceptibility mapping and prediction. The publication pattern reveals that most research outputs are from Asian countries like China and India, reflecting their vulnerability to such disasters. Most studies used slope angles as a geological factor, while rainfall is the most common triggering factor in landslide susceptibility and prediction. Additionally, with technological advancement, artificial intelligence is effective in landslide susceptibility mapping and prediction with increasing efficiency. Furthermore, this work provides valuable insights for developing region-specific landslide mitigation strategies and underscores the potential of interdisciplinary approaches combining geology, meteorology, anthropogenic, and artificial intelligence for effective disaster management.