ArticleScientific reports2026
GIS-based landslide susceptibility zonation using weighted overlay analysis with AHP: a case study of Malappuram district, Kerala, India.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Malappuram district, Kerala, ranked 7th among the most landslide-prone districts in India, according to the Landslide Atlas of India 2023 from the National Remote Sensing Centre (NRSC). The aim of this study is to map landslide susceptibility zones at the district level in Malappuram, Kerala, using GIS-based Weighted Overlay Analysis (WOA). The most commonly used factors for slope failure preparation were slope, elevation, aspect, curvature, land use/land cover, annual rainfall, distance to road, distance to river, soil depth, geology, LS factor, drainage density, Stream Power Index, and Topographic Wetness Index. The AHP technique has been applied to weight the 14 conditioning factors, and Pearson correlation has been used to assess the relation among these variables and between these variables and the landslide points. We have found that most variables are independent, with a maximum correlation coefficient of 0.72. Five respondents ranked 14 conditioning variables using a structured AHP matrix in a paired comparison. The Consistency Ratio obtained is 0.047, which is < 0.10. Slope was given the highest weightage (18%), followed by geology (13%) and annual rainfall (10%), which have the highest contributions to slope failures. The 14 conditioning factors were reclassified into five categories with values ranging from 1 to 5. The 14 classified factors were then combined to produce the landslide susceptibility map of the study area. The landslide susceptible maps were classified into low (39%, ~ 1384 km
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