Evidence map›Paper›PMID 41476099›Full record

ArticleScientific reports2025

National-scale landslide susceptibility and risk mapping of India using a hybrid data-driven approach.

Imran Khan, Harish Bahuguna, Ashutosh Kainthola, Rayees Ahmed, Md Sarfaraz Asgher

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Detoxification of organic pollutants over novel niobium and cobalt codoped molybdenum oxy-sulfide photocatalyst under visible light.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2026
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Imran KhanGeoengineering and Computing Laboratory, Department of Geology, Banaras Hindu University, Varanasi, 221005, India.ORCID http://orcid.org/0000-0001-7377-5251
Harish BahugunaGeological Survey of India, CHQ Kolkata, Kolkata, 700016, India.
Ashutosh KaintholaGeoengineering and Computing Laboratory, Department of Geology, Banaras Hindu University, Varanasi, 221005, India. ashutosh.geo@bhu.ac.in.
Rayees AhmedDivecha Center for Climate Change, Indian Institute of Science Bangalore, Bangalore, 560012, India. rayeesrashid84@gmail.com.ORCID http://orcid.org/0000-0003-3192-7258
Md Sarfaraz AsgherDepartment of Geography, University of Jammu, Jammu, 180006, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Landslides pose a persistent and deadly hazard across India's diverse terrain, demanding robust, high-resolution national-scale assessments to guide effective risk mitigation and policy decisions. This study addresses a critical research gap in landslide susceptibility and risk assessment across India at a high spatial resolution of 90 m × 90 m on a national scale. Leveraging a robust landslide inventory comprising 109,504 documented events and incorporating causative factors selected through expert judgment, three models Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Yule's Coefficient (Yc) were employed to reduce model bias and identify the most reliable susceptibility and risk mapping approach through comprehensive field validation and map interpretation. The results indicate, very high landslide susceptibility zones cover 4.0% (AHP), 4.2% (Yc), and 4.5% (FR) of India's land area, while combined high and very high susceptibility classes account for 11.0% (AHP), 10.4% (Yc), and 10.5% (FR). Model evaluation demonstrates robust predictive performance, with accuracies of 89.1% (AHP), 88.9% (Yc), and 90.5% (FR) according to the ROC curve, and 86.7%, 85.6%, and 87.2% respectively via the precision-recall curve. Minor variations in susceptibility extent and accuracy among models underscore the resilience of the input data, however the Yc model provides a a more ground representative output based on factor weighting. Utilizing the Yc model and exposure data, approximately 8,606 km² (0.26% of India) are classified as having a high to extremely high landslide risk at a qualitative scale. Overall, the susceptibility analysis indicates that approximately 0.34 million km² is categorized as having high and very high susceptibility under Yc model. The analysis identifies Nagaland (55%), Mizoram (53.1%), Arunachal Pradesh (52.1%), Sikkim (45.0%), Uttarakhand (40.0%), Manipur (30.6%), Himachal Pradesh (28.9%), Meghalaya (22.0%), Jammu & Kashmir (20.2%), and Tripura (8.0%) as the ten most landslide-susceptible States and Union Territories , listed in descending order of susceptibility. These findings provide critical insights for disaster risk reduction strategies and policymaking, enhancing landslide prediction, mitigation, and resilience planning across susceptible regions at mapped scale.

Indexed as

Geospatial analysisHybrid modelingIndiaLandslide susceptibilityRisk assessment

Identifiers

PMID41476099
PMCPMC12847720

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.