ML and GIS approach for Landslide Hazard Assessments in Lesser Himalaya, India

Geodynamics of Himalaya is always associated with intense rainfall, aggressive slopes, fragile lithology, and active tectonism, in which linear infrastructure expansion has combined and amplify the landslide risk at threshold levels. 15 % land area of India (including snow cover) is prone to landslide hazards, in which Uttarakhand state is the most susceptible part of the country. According to the Geological Survey of India, Uttarakhand, has witnessed 4,654 landslides, 92 avalanches, 67 cloudbursts and 12,758 flood events which resulted as over 1,200 fatalities and 1.3 billion US dollars damage between 2015 to 2025. This underlines a major wake call for the geoscientists and policy makers regarding settlements in and around himalayan regions.

This work focuses on preparing a Landslide Hazard Zonation (LHZ) map/model for the NH-109K corridor of Lesser Himalaya, India using Geographic Information System (GIS) and Machine Learning (ML) techniques. The approach involves integrating multiple geo-environmental and terrain parameters that influence slope-instability. The primary thematic layers considered in this study include slope, aspect, rainfall, normalized difference vegetation index (NDVI) and land use/land cover (LuLc). Additional factors such as lithology, drainage density, proximity to roads, and rainfall are also incorporated. SRTM DEM and Sentinel 2 satellite imagery are used to derive topographic and derivative parameters, while rainfall and landslide inventory are obtained from Indian Meteorological Department and Bhukosh portal (Open-source data archive of Geological Survey of India). The thematic layers are standardized, weighted, and integrated within the GIS environment and simulated into data driven Machine Learning environment to establish their spatial association with observed landslide occurrences. Through this integration, the study aims to delineate zones exhibiting varying degrees of landslide prone across the NH-109K.

The resulting LHZ map categorizes the area into five susceptibility zones (very high, high, moderate, low and very low) reflecting the degree of terrain instability. The work emphasizes the significance of ML techniques in assessing complex natural hazards like landslides. Such an approach contributes to informed decision-making for infrastructure development and hazard mitigation in mountainous regions. Adopted methodologies also holds potential for replication in other mountain-corridors facing similar geomorphic and climatic conditions. Thus, this study supporting sustainable and resilient road network planning in landslide-prone areas with special reference to the Lesser Himalayan belt of India.

Keywords: GIS, Landslide, Lesser Himalaya and Machine learning (ML).