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 2), moderate (51%, ~ 1811 km2), and high landslide susceptibility zones (10%, ~ 355 km2). 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.