Asian Journal of Environment & Ecology

Growing awareness of landslide impacts and the need for urban development in challenging mountainous terrain has increased scientific interest in LSZ mapping. Landslide hazard assessment and mapping are crucial processes in understanding and mitigating landslide-related risks. Sensitivity assessment regarding the spatial division of landslide-prone areas depending on the topographic-ecological situation. Wayanad is prone to unexpected landslides due to human interventions, unusual geological and abundant rainfall, which cause loss of life and property damage. The objective of this research is to explore the effectiveness of using the frequency ratio model and prediction rate to analyse the landslide hazard of the Kamati River, a tributary of the Cauvery River flowing through southern India. The study was conducted to construct a landslide susceptibility map of the Kabani River Basin area in the Southern Western Ghats region using a statistical method. For this, we used previously recorded landslide locations and eleven landslide factors were used for modelling, namely lithology, geomorphology, slope angle, soil texture, distance from streams, distance from roads, distance from landmarks, topographic wetness index (TWI), rainfall, land use/land cover, and slope curvature, which were extracted from the spatial database. Initially, the study presents a very comprehensive approach by mapping landslide-prone areas using relative frequency and prediction rate, which generates a Landslide Prone Area Index (LSI) and a susceptibility map. Furthermore, the study reveals that the southwest part of the study area is prone to landslides due to the extensive influence of 65° slope, intense rainfall, soil texture, topography moisture index, curvature, lithology and geomorphology. It also includes the distance to roads, lines and streams. The integration of diverse factors such as climatology, topography, hydrology, and geology demonstrates a holistic approach used in the vulnerability assessment, which helps to more accurately identify areas at risk in the Kabani River Basin. The correlation between slope, soil type, and historical landslide locations confirms the effectiveness of this geospatial model in identifying hazardous areas, aiding disaster mitigation, and land use planning in the Kabani River Basin. The predicted pattern is highly similar to the area where landslides have occurred in the past, and it helps in future conservation planning and sustainable land use planning to mitigate landslide risk in the southern Western Ghats.