GIS-based landslide susceptibility zonation using weighted overlay analysis with AHP: a case study of Malappuram district, Kerala, India

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 2 ), moderate (51%, ~ 1811 km 2 ), and high landslide susceptibility zones (10%, ~ 355 km 2 ). The high landslide susceptible areas are distributed over, in, and around the north-east highland of the district and are characterised by a combination of high slopes, charnockite geology, and high rainfall. The model has been validated using historical landslides point and random non-landslide points. The ratio of landslide and non-landslide points is 70:30 for training and testing, respectively. We obtained AUC values of 0.921 and 0.897 for training and testing, respectively. At the village level, the most affected villages due to landslides have been selected based on the percentage of land area falling within the highly landslide susceptible zones among all 135 revenue villages of the district. The most susceptible villages, in descending order, are Kerala Estate, Chokkad, Akampadam, Karulai, and Kurumbalangode. A detailed landslide susceptibility map of Malappuram district will enable scientists and authorities to plan mitigation actions, regulate construction activities in highly affected areas, and implement an early warning system at the local level.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-06-16
DOI
https://doi.org/10.1038/s41598-026-57794-7
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GIS-based landslide susceptibility zonation using weighted overlay analysis with AHP: a case study of Malappuram district, Kerala, India

B. Senthil Rathi, P. Senthil Kumar, K. Shanu, Gayathri Rangasamy
Scientific Reports
Landslides and related hazards
article

GIS-based landslide susceptibility zonation using weighted overlay analysis with AHP: a case study of Malappuram district, Kerala, India

B. Senthil Rathi, P. Senthil Kumar, K. Shanu, Gayathri Rangasamy
article en

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 2 ), moderate (51%, ~ 1811 km 2 ), and high landslide susceptibility zones (10%, ~ 355 km 2 ). The high landslide susceptible areas are distributed over, in, and around the north-east highland of the district and are characterised by a combination of high slopes, charnockite geology, and high rainfall. The model has been validated using historical landslides point and random non-landslide points. The ratio of landslide and non-landslide points is 70:30 for training and testing, respectively. We obtained AUC values of 0.921 and 0.897 for training and testing, respectively. At the village level, the most affected villages due to landslides have been selected based on the percentage of land area falling within the highly landslide susceptible zones among all 135 revenue villages of the district. The most susceptible villages, in descending order, are Kerala Estate, Chokkad, Akampadam, Karulai, and Kurumbalangode. A detailed landslide susceptibility map of Malappuram district will enable scientists and authorities to plan mitigation actions, regulate construction activities in highly affected areas, and implement an early warning system at the local level.

Scientific Reports
Pondicherry University (IN), Korea University (KR), Sri Ramachandra Institute of Higher Education and Research (IN), Korea University (JP), Karpagam Academy of Higher Education (IN), Sri Sivasubramaniya Nadar College of Engineering (IN)
Climate action
Openalex Percentile: Top 5%
Landslides and related hazards
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.