The work was led by Ankit Singh, Nitesh Dhiman and Dericks Praise Shukla, who set out to address a gap they identify in previous research: most earlier studies of the IHR focused on susceptibility mapping alone, while regional-scale risk assessment has remained scarce. Susceptibility mapping identifies where landslides are likely to occur, but it does not account for the presence of population, road infrastructure or the preparedness of affected areas. By adding vulnerability measures, the authors argue, the resulting risk picture becomes far more useful for urban planning and disaster risk reduction in mountainous terrain.

The IHR is described in the study as among the most tectonically active and dynamic mountain regions in the world, which underlies the persistent landslide hazard there. The susceptibility component of the analysis used machine learning-based modelling, and its results showed that 8.32% of the region — 44,400 square kilometres — was classified as very high or high susceptibility. These susceptible zones were mainly concentrated in the states of Himachal Pradesh and Uttarakhand.

The vulnerability assessment revealed what the authors describe as spatial heterogeneity in vulnerability indicators across the IHR, meaning that exposure and capacity to respond vary considerably from one area to another. When susceptibility and vulnerability were combined into the final risk results, the district-level ranking placed Mandi, Tehri Garhwal, Solan, Almora and Bilaspur as the top five districts with the largest area under high and very high risk. This district-level detail is intended to help authorities prioritise where mitigation measures and preparedness investments are most needed.

To make the findings accessible beyond the research community, the risk results are presented through a web application built on Google Earth Engine, aimed at policymakers, planners and the general public. The authors state that the inclusion of vulnerability parameters alongside machine learning-based susceptibility results provides a workflow for identifying risk areas, which they say will assist disaster risk reduction and sustainable development in the region. The full article is available in Natural Hazards, Volume 122, article number 705, published on 27 October 2026.

Subject of Research: Landslide susceptibility and risk assessment across the Indian Himalayan Region

Article Title: Landslide susceptibility and risk estimation across the Indian himalaya for informed decision-making

Article References: Singh, A., Dhiman, N., & Shukla, D. P. (2026). Landslide susceptibility and risk estimation across the Indian himalaya for informed decision-making. Natural Hazards, 122(22), 705. https://doi.org/10.1007/s11069-026-08482-2

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08482-2

Keywords: landslides, Indian Himalaya, susceptibility mapping, risk assessment, vulnerability, machine learning, disaster risk reduction, Uttarakhand, Himachal Pradesh, Google Earth Engine, urban planning, Landslide