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dc.contributor.authorSujatha, Evangelin Ramanien
dc.contributor.authorSridhar, Venkataramanaen
dc.identifier.citationSujatha, E.R.; Sridhar, V. Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India. Hydrology 2021, 8, 41.en
dc.description.abstractLandslides are a common geologic hazard that disrupts the social and economic balance of the affected society. Therefore, identifying zones prone to landslides is necessary for safe living and the minimal disruption of economic activities in the event of the hazard. The factors causing landslides are often a function of the local geo-environmental set-up and need a region-specific study. This study evaluates the site characteristics primarily altered by anthropogenic activities to understand and identify the various factors causing landslides in Coonoor Taluk of Uthagamandalam District in Tamil Nadu, India. Studies on landslide susceptibility show that slope gradient, aspect, relative relief, topographic wetness index, soil type, and land use of the region influence slope instability. Rainfall characteristics have also played a significant role in causing landslides. Logistic Regression, a popular statistical tool used for predictive analysis, is employed to assess the various selected factors’ impact on landslide susceptibility. The factors are weighted and combined in a GIS platform to develop the region’s landslide susceptibility map. This region has a direct link between natural physical systems, hydrology, and humans from the socio-hydrological perspective. The landslide susceptibility map derived using the watershed’s physical and environmental conditions offers the best tool for planning the developmental activities and prioritizing areas for mitigation activities in the region. The Coonoor region’s tourism and agriculture sectors can significantly benefit from identifying zones prone to landslides for their economic stability and growth.en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.titleLandslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, Indiaen
dc.typeArticle - Refereeden
dc.description.versionPublished versionen
dc.contributor.departmentBiological Systems Engineeringen

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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International