Landslide Susceptibility Assessment: Drivers, Trends and Data-Driven Approaches

Landslides are among the most destructive geohazards worldwide, accounting for a significant proportion of natural disaster-related fatalities and economic losses. The increasing frequency of landslides in India, driven by climate change, extreme weather events, and anthropogenic activities, underscores the need for accurate landslide susceptibility assessment and risk mitigation strategies. This review highlights the key drivers behind the occurrence of a landslide including topographic, geological, hydrological, climatic, and humaninduced factors, and examines recent trends influencing landslide hazards. Particular emphasis is placed on the role of intensifying cyclonic activity and ocean warming in the North Indian Ocean, which contribute to extreme rainfall events and heightened slope instability. A bibliometric analysis of 897 Scopus-indexed publications (2015–2025) reveals a rapid expansion of landslide susceptibility research, driven by advances in geographic information systems (GIS), remote sensing, machine learning, and artificial intelligence. Furthermore, qualitative, semi-quantitative, and quantitative landslide susceptibility mapping (LSM) approaches are critically compared in terms of their strengths, limitations, data requirements, uncertainty handling, and practical applications. The review highlights the growing adoption of machine learning and hybrid modelling techniques for improving prediction accuracy while emphasizing the importance of integrating multi-source geospatial datasets and climate-related variables. The findings provide an updated perspective on landslide susceptibility assessment and offer insights for future research, disaster risk reduction, and sustainable land-use planning in landslide-prone regions.