The research team, led by Kiker Singh of Kurukshetra University with colleagues from Banaras Hindu University, Netaji Subhash University of Technology and NIIT University, focused on a section of the Lesser Himalaya that is tectonically restless and structurally fragmented. Three major regional thrust faults, the Giri, Chail and Juthogh thrusts, cut across the lower, middle and upper parts of the study area, fracturing the rock and priming slopes for failure. Elevations along the corridor range from roughly 1,425 to 1,700 metres above sea level, and the entire region sits within India’s seismic zone IV, meaning strong earthquake shaking is a realistic possibility. Add intense monsoon rainfall and aggressive road widening, and the ingredients for disaster are all present.

The stakes are not abstract. India accounts for nearly fifteen percent of all reported landslide occurrences worldwide and about thirty-one percent of associated fatalities, and almost 0.31 million square kilometres of landslide-prone territory lies within the Himalayan belt. Globally, landslides cause billions of dollars in economic losses and thousands of deaths each year, and roughly 50,000 people are estimated to have died from rock slope failures during the twentieth century alone. Along the Solan-Shimla stretch, road widening has repeatedly disturbed the natural equilibrium of slopes by removing the toe of hillsides, and since that work began, two lanes of the highway have been completely or partially blocked by landsliding between Solan and Kaithlighat.

To build their map, the researchers started with a landslide inventory: forty-eight landslide locations recorded along the highway using handheld GPS, cross-checked against high-resolution imagery in Google Earth and verified during extensive field campaigns. Each landslide was delineated as a polygon and then converted to point data within a geographic information system. This inventory served as the ground truth against which the model’s predictions could be tested, a step that many older susceptibility studies skipped and one that lends the new work considerable credibility.

The heart of the method is the Analytic Hierarchy Process, a multi-criteria decision-making framework introduced by mathematician Thomas Saaty. AHP asks experts to compare every causative factor against every other in pairs, assigning relative importance on a scale from one to nine. The resulting comparison matrix yields weights through eigenvector analysis, and a consistency check ensures the judgements are internally coherent. In this study the consistency ratio came out at 0.079, comfortably below the 0.10 threshold that signals unacceptable inconsistency. The team then combined the weighted factor layers in ArcGIS to produce a continuous susceptibility index for every patch of terrain along the corridor.

What distinguishes this study from earlier work in the same region is the breadth of factors considered. Previous research had examined slope, curvature, relative relief, aspect, geology, lithology, fault density, distance to road and drainage density. The new analysis adds five previously unexplored variables: the Topographic Wetness Index, which quantifies where water accumulates in soil; the Stream Power Index, which captures the erosive force of flowing water; land use and land cover; geomorphology; and lineament density, a measure of weakness zones in the rock. All terrain-derived layers were generated from a 30-metre Shuttle Radar Topography Mission digital elevation model, while geological and geomorphological data came from the Geological Survey of India’s Bhukosh portal.

The results are striking in their clarity. Slope emerged as the single most influential factor, receiving a weight of about 30.56 percent, with the densest landslide activity concentrated on slopes between 30 and 60 degrees. Distance to road ranked second at roughly 18.68 percent, confirming what engineers have long suspected: cutting into hillsides to widen the highway is a primary trigger of instability. Geology followed at about 9.5 percent, with the Mahi-Krol A and Chhaosa formations, composed of weathered limestone, shale and siltstone, accounting for a third and a quarter of all mapped landslides respectively. Concave slopes, which trap water and raise pore pressure, hosted 62.5 percent of the events, and highly dissected structural hills experienced nearly 73 percent of them. Aspect mattered too: northeast-facing slopes alone recorded about 29 percent of the landslides, likely because uneven solar exposure alters soil moisture dynamics.

When the weighted layers were merged and classified into five categories using natural breaks, the map revealed that 12.83 percent of the study area falls into the very high susceptibility class and a further 21.98 percent into the high class. Moderate susceptibility covers 26.58 percent, while low and very low zones account for 24.02 and 14.57 percent respectively. The validation statistics are what make the map genuinely useful rather than merely illustrative. The model achieved a ROC-AUC of 0.835, placing it in the ‘very good’ range, and a Precision-Recall AUC of 0.943, which is particularly meaningful because precision-recall analysis is less prone to overestimating performance when landslide events are rare compared with stable ground. An F1-score of 0.923 at an optimal threshold of 2.664 confirmed strong classification reliability.

Spatial validation sealed the case. Of the forty-eight mapped landslides, 72.92 percent occurred in the very high susceptibility zone and 25 percent in the high zone, with just one event, or 2.08 percent, in the moderate class and none at all in the low or very low categories. Normalized landslide density climbed steadily with susceptibility class, peaking at 0.821 in the very high zone. In practical terms, the model rarely cries wolf and almost never misses a real hazard, which is precisely the behaviour a planning authority needs when deciding where to install slope protection, drainage works or early warning systems.

The authors are candid about the limitations. An inventory of forty-eight events is modest, expert-driven weighting in AHP inevitably carries subjectivity, and the linear corridor format restricted any detailed rainfall threshold analysis. Future work, they note, will integrate rainfall data to build event-triggered models and apply machine learning approaches with expanded inventories to sharpen predictions further. Even so, the map already offers immediate value: engineers can prioritise slope stabilisation and cut-slope reinforcement along the highest-risk sections, adopt bio-engineering measures such as deep-rooted vegetation and grass turfing in moderate zones, and manage surface and subsurface drainage to keep water out of slope material. For a highway that thousands of people depend on every day, knowing precisely where the mountainside is most likely to move may prove as valuable as any retaining wall.

Subject of Research: Landslide susceptibility mapping along the Solan-Shimla National Highway-5 corridor in Himachal Pradesh, India, using GIS and the analytic hierarchy process

Article Title: Landslide susceptibility mapping along the Solan-Shimla highway using the analytic hierarchy process, Himachal Pradesh, India

Article References: Singh, K., Kumar, S., Khan, I., Kumar, H., & Rawat, A. (2026). Landslide susceptibility mapping along the Solan-Shimla highway using the analytic hierarchy process, Himachal Pradesh, India. Discover Geoscience, 4(1), Article 327. https://doi.org/10.1007/s44288-026-00694-0

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00694-0

Keywords: landslide susceptibility, analytic hierarchy process, Himachal Pradesh, National Highway-5, Himalaya, GIS, remote sensing, Solan, Shimla, slope stability, ROC-AUC, geohazard mapping