The research team, led by Anannya Panging of Mizoram University together with colleagues from institutions across India, focused on the Dikhow River Basin, a drainage system covering roughly 3,971 square kilometres. The river rises near Zunheboto peak in Nagaland and flows north through Assam before joining the Brahmaputra, its basin spanning steep mountainous country in the south and flat alluvial plains in the north. Geologically, the upper basin is built from the Disang, Barail and Tipam groups of Eocene to Miocene age, dominated by sandstones, shales and clays, materials that are notoriously fragile when saturated by the region’s intense monsoonal rainfall. Nagaland falls within Zone V, the highest category, of India’s landslide hazard zonation map, yet no previous study had ever assessed landslide susceptibility across this basin.

To build their maps, the researchers compiled an inventory of 632 landslides identified from Google Earth imagery, then split the records into a training set of 442 points and a testing set of 190 points in a 70:30 ratio. They paired this inventory with twelve landslide conditioning factors: slope, aspect, elevation, rainfall, lithology, lineament density, distance to streams, the Normalized Difference Vegetation Index, the Topographic Wetness Index, land use and land cover, the Stream Power Index, and curvature. Each factor was derived from publicly available data, including the Shuttle Radar Topography Mission digital elevation model, Geological Survey of India lithological records, gridded rainfall data from 2003 to 2021, and 10-metre land cover imagery. All layers were resampled to a common 30-metre resolution and projected into a single coordinate system before analysis.

The two competing techniques were the Frequency Ratio model and the Index of Entropy model, both bivariate statistical approaches that quantify the association between past landslide locations and each class of each conditioning factor. The Frequency Ratio method is elegantly simple: for every class of every factor, it calculates the ratio of the proportion of landslide pixels to the proportion of the total area. A value above one signals a positive correlation with landsliding, below one a negative one. The Index of Entropy, by contrast, borrows Shannon’s information theory to assign weights that account for the uncertainty, or disorder, in the distribution of landslides across factor classes, producing a more elaborate weighting scheme. Both models are prized for their transparency and their ability to reveal threshold values, an advantage over black-box machine learning approaches when the goal is to explain why certain slopes fail.

Before running the models, the team checked for multicollinearity among the twelve factors using the Variance Inflation Factor and Tolerance statistics. Values ranged from 1.059 to 2.024 for VIF and 0.494 to 0.944 for Tolerance, well within acceptable limits, confirming that no factor was redundant and all twelve could legitimately enter the analysis. The relative importance of the factors then diverged between the models in an instructive way. In the Frequency Ratio framework, land use and land cover emerged as the single most influential factor, followed by slope, lithology and the Topographic Wetness Index. The Index of Entropy instead gave top weighting to the Topographic Wetness Index, with slope and land cover nearly tied at around 0.34. The discrepancy reflects the different mathematical machinery behind the two methods and hints that landslides here are not driven by any single cause but by interacting combinations of terrain, geology, hydrology and human activity.

The spatial patterns the models uncovered are striking. Landslides clustered in intermediate elevations between roughly 329 and 1,589 metres, on steep slopes where frequency ratios climbed as high as 3.19 in the steepest class of 35 to 71 degrees. Concave slopes, which accumulate water and raise pore pressure, showed a frequency ratio of 1.71, while carbonaceous sandy shale proved the most dangerous lithology with a ratio of 2.13, its moderate permeability and the chemical weathering of carbonate making it especially unstable. Counterintuitively, some of the lowest rainfall zones showed the strongest landslide associations, likely because those areas combine high lineament density, low vegetation cover and steep terrain, while the wettest zones sit largely on the stable plains of Assam. Built-up areas and rangeland, where human slope modification strips away protective vegetation, both showed positive associations with landsliding, underscoring the growing role of anthropogenic pressure on the region’s slopes.

When the two susceptibility maps were assembled and classified into five zones from very low to very high using natural-break classification, the differences between the models became vivid. The Frequency Ratio model flagged 19.9 percent of the basin as very highly susceptible and another 34.1 percent as highly susceptible, whereas the Index of Entropy assigned only 8.4 percent to the very high class and 22.7 percent to the high class. In both maps, the danger zones concentrated in the southern, south-eastern and south-western portions of the basin, particularly the steep uplands of Nagaland, with the districts of Tuensang, Mon and Longleng bearing the brunt. The northern, downstream plains registered low to very low susceptibility in both models, a reassuring result for the densely settled valley areas near the Brahmaputra confluence.

Validation separated the two contenders decisively. Using the area under the receiver operating characteristic curve, the gold-standard measure of predictive skill, the Frequency Ratio model scored 0.81 on the training data and 0.82 on the testing data, both firmly in the very good range. The Index of Entropy achieved 0.79 and 0.80 respectively, respectable but consistently a step behind. A second validation metric, the relative landslide density or R-index, told the same story: 64.81 percent of recorded landslides fell within the very high susceptibility zone of the Frequency Ratio map, compared with 60.25 percent for the entropy map, and landslide density rose steadily from low to high classes in both, confirming that the maps captured real spatial structure rather than statistical noise. The researchers attribute the entropy model’s slight underperformance to the narrow range of its factor weights in a basin where landslides respond to complex, interacting conditions rather than any single dominant trigger.

The practical implications extend well beyond academic comparison. The very high and high susceptibility zones identified by the Frequency Ratio map cover the upper Dikhow basin, precisely where road connectivity has repeatedly been severed and where landslides have claimed lives and destroyed property. The authors position the maps as a preliminary screening tool for hazard management and early warning systems, one that government agencies, policymakers and land-use planners can use to prioritise slope stabilisation, regulate construction on fragile hillsides and route infrastructure away from the most dangerous terrain. The method itself, built entirely from freely available satellite and government data, is transferable to other data-scarce mountainous regions facing similar monsoon-driven slope failures.

The study is candid about its limits. Seismic factors were excluded because ground data were unavailable, despite the region’s tectonic activity along features such as the Naga Thrust, and the temporal and spatial resolution of remote sensing data constrains the precision of any inventory-based mapping. The authors call for site-specific geotechnical investigation as the next step and point toward physics-informed machine learning and transfer learning techniques, which can carry knowledge from data-rich regions into data-poor ones, as the future of susceptibility modelling in the Northeast. For now, though, the message from the Dikhow basin is clear: sometimes the simplest statistical tool, applied carefully to good data, beats its more sophisticated rivals, and in doing so can hand a vulnerable region its first real map of where the earth is most likely to move.

Subject of Research: Comparative GIS-based landslide susceptibility mapping of the Dikhow River Basin using frequency ratio and index of entropy statistical models

Article Title: Comparative evaluation of frequency ratio and index of entropy models for landslides susceptibility mapping of Dikhow river basin, Northeast India

Article References: Comparative evaluation of frequency ratio and index of entropy models for landslides susceptibility mapping of Dikhow river basin, Northeast India. (n.d.). https://doi.org/10.1007/s44288-026-00737-6

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00737-6

Keywords: landslide susceptibility mapping, frequency ratio, index of entropy, Dikhow River Basin, Nagaland, Northeast India, GIS, bivariate statistical models, natural hazards, monsoon rainfall, slope instability, ROC-AUC validation

The Western Ghats are naturally susceptible to rainfall-triggered landslides because several risk factors overlap. The region has steep, deeply dissected terrain, weathered basaltic rocks and soil-like regolith that can lose strength when saturated. Miraswadi receives approximately 1,733 millimetres of rain annually, much of it during the southwest monsoon. During prolonged or unusually intense storms, water infiltrates the slope, raises groundwater levels and increases pore-water pressure between grains. That pressure reduces the friction and effective stress holding the material together, lowering its shear strength. Surface runoff can then concentrate in channels, erode exposed soil and mobilize loose debris. The July 2021 event occurred during an extreme rainfall episode that triggered thousands of landslides across Maharashtra’s Western Ghats. The local landscape also includes agricultural terraces, settlements, drainage modifications and other human changes that can redirect runoff or alter infiltration. The researchers did not identify these activities as the primary cause of the Miraswadi failure, but they concluded that such modifications may influence local susceptibility when combined with steep slopes, weathered materials and exceptional rainfall. Seismic activity in the nearby Koyna–Varna zone may contribute to long-term fracturing, although rainfall remains the principal trigger in this setting.

To reconstruct the terrain, the team flew a DJI Phantom 4 Pro equipped with a 20-megapixel, one-inch sensor camera at roughly 200 metres above ground level. The mission collected 283 georeferenced high-resolution images, with about 80 per cent forward overlap and 30 per cent side overlap between photographs. Overlapping images allow photogrammetry software to identify the same surface points from multiple viewpoints and calculate their three-dimensional positions. The resulting dataset produced an orthomosaic, a digital elevation model, contour information and a dense point cloud containing approximately 31 million points. Its mean ground sampling distance was 5.52 centimetres, meaning that individual image-derived cells represented only a few centimetres of ground surface. Image calibration was reported as 100 per cent successful, with a mean reprojection error of approximately 0.143 pixels. Handheld GPS measurements supplied ground control points to improve positional consistency and elevation calibration. The researchers emphasize that the products were intended primarily for relative geomorphological analysis rather than survey-grade geodetic measurement. Recreational-grade GPS accuracy and the absence of a pre-failure drone survey limit the precision with which surface movement and lost volume can be interpreted. Even so, the model provided sufficient detail to identify cracks, scarps, slumps and debris pathways across a hazardous slope.

The mapped landslide began approximately 125 metres northeast of Miraswadi on 23 July 2021. Its visible failure area covered about 1.34 hectares, while the associated debris spread extended across roughly 2.95 hectares. From crown to toe, the movement reached approximately 255 metres, with widths ranging from 112 to 162 metres. The researchers estimated that between 42,050 and 46,076 cubic metres of material had been displaced, presenting the result as a range because the pre-failure surface had to be reconstructed from surrounding undisturbed terrain. Morphological evidence suggests that the event began as a debris slide, in which weathered soil and rock moved downslope as a relatively coherent mass. Once the material entered a pre-existing second-order drainage channel, it became more confined and flow-like, evolving into a channelized debris movement. The terrain model divided the slope into an initiation zone between approximately 747 and 690 metres above mean sea level, a transport zone from about 690 to 665 metres, and a deposition zone from roughly 665 to 642 metres. The upper initiation area included steep slopes exceeding 45 degrees and locally surpassing 60 degrees. Lower gradients allowed transported soil, rock fragments and uprooted vegetation to accumulate across the broader depositional area.

The most consequential discovery was not confined to the old landslide scar. Immediately upslope of the habitation, researchers identified a deformation area containing three to four major tension cracks between approximately 70 and 90 metres long and up to half a metre wide. Several cracks coincided with localized ground settlement and slumping, with vertical displacement reaching about 0.5 metres. Their roughly slope-parallel alignment and position on a hillside directly above homes indicate that the ground has continued to adjust after the 2021 failure. A second area, above the original crown, contained two or three smaller cracks approximately 10 to 15 metres long and 0.1 metres wide. These may represent instability propagating upslope, although the study does not establish a precise rate or direction of movement. Field inspections confirmed active slumping, regolith displacement, minor scarps, surface undulations and additional cracks within the weathered soil. Tilting vegetation supplied another visible sign that the ground beneath roots may be shifting. The researchers carefully distinguish these observations from pre-failure warning signs: because the survey was conducted after the landslide, the features document ongoing post-failure deformation rather than proven precursors. Their location nevertheless matters for risk assessment. A hazard map drawn only around the original scar could overlook unstable ground that threatens the settlement from above.

The team also compared land use in satellite imagery from 2011 and 2022 to examine how the surrounding landscape had changed. Settlement area increased from approximately 8,014 square metres to 15,403 square metres, an expansion of about 92 per cent. Over the same period, terrace farming declined from about 454,746 to 409,114 square metres, while barren or fallow land decreased from approximately 115,372 to 105,709 square metres. Forest cover increased from about 170,695 to 218,601 square metres, suggesting that vegetation expanded overall even as localized changes occurred near the hamlet. The researchers observed the growth of residential structures, modifications to agricultural terraces and exposed soil surfaces, particularly on slopes northeast of the settlement where active instability was detected. These changes can affect how water travels across a hillside. A building platform, track, terrace or altered drainage line may concentrate runoff in one place, increase infiltration in another or remove material that previously protected the soil. The study does not claim that settlement growth caused the landslide. Instead, it presents human landscape modification as a factor that can interact with natural controls and increase exposure. In a small rural community, even modest expansion can place more homes, fields and livestock facilities beneath an unstable slope.

The findings have immediate implications for monitoring and preparedness, but the authors caution against treating their preliminary recommendations as final engineering plans. The deformation zone above the hamlet should receive continued attention, particularly during and after periods of intense rainfall. Repeated drone surveys could compare successive digital elevation models and orthomosaics to detect crack widening, new scarps, changing vegetation tilt or accelerated ground displacement. Field observations remain essential because dense vegetation, shadows and image geometry can obscure features in aerial data. Surface drainage management may reduce water concentration and infiltration in unstable areas, while vegetation-based measures could help control erosion and reinforce shallow soil. Retaining structures might be appropriate in selected locations, but their design would require detailed geotechnical, hydrogeological and engineering investigations. The study also identifies a potential temporary refuge area chosen using topography, distance from unstable slopes, access and proximity to agricultural and livestock resources. That site is not validated as a permanent rehabilitation location; no dedicated land-suitability, geotechnical or hydrogeological assessment was performed. Because many residents depend on farming and livestock and maintain strong ties to their homes, temporary evacuation during extreme rainfall may be more realistic than immediate permanent relocation. Warning signs such as widening cracks, renewed slumping or rapidly increasing deformation could help guide such decisions.

Miraswadi illustrates both the power and the limits of high-resolution remote sensing in landslide science. Regional susceptibility maps can identify broad patterns of danger, but they cannot always show whether a particular crack runs behind a house, whether a drainage channel links a scar to a settlement or whether deformation extends beyond a mapped failure. A drone can be deployed quickly over a relatively small area and generate detailed terrain information without exposing surveyors to the most hazardous ground. Yet a single post-event flight cannot reveal how fast the slope is moving, what is happening underground or whether another failure will occur. The study therefore calls for repeated UAV acquisitions combined with rainfall records, hydrological monitoring, geotechnical testing, geophysical surveys and analysis of rainfall thresholds. Such integration could turn a detailed snapshot into an early-warning system. For communities across the Western Ghats and other tropical mountain regions, the approach offers a practical way to document landslides after extreme storms and identify danger zones that remain active after the debris has stopped moving. In Miraswadi, the central message is straightforward: the end of a landslide’s visible movement does not necessarily mean the slope has stabilized. High-resolution mapping can make that hidden continuation visible before the next monsoon tests the hillside again.

Subject of Research: UAV mapping of landslide morphology and post-failure slope instability in Miraswadi, India

Article Title: UAV based assessment of landslide morphology and slope instability in Miraswadi, Western Ghats, India

Article References: Shirke, A. V., Khandge, A., Umrikar, B. N., & Asim, M. (2026). UAV based assessment of landslide morphology and slope instability in Miraswadi, Western Ghats, India. Discover Geoscience, 4(1), Article 334. https://doi.org/10.1007/s44288-026-00707-y

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00707-y

Keywords: landslides, UAV mapping, Western Ghats, slope instability, debris flows, remote sensing, monsoon rainfall, hazard mitigation, based, assessment, landslide, morphology