GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
Blog Article
In the Nilgiris district of the Western Ghats, landslides pose a recurrent threat, necessitating accurate landslide susceptibility mapping (LSM) to designate high-risk zones and mitigate the potential loss of lives and property.Kundah taluk in the Nilgiris has witnessed an increasing number of landslide events in recent years, primarily attributed to heavy monsoon rainfall, steep terrain and human-induced factors.This highlights the necessity for mapping landslide susceptibility and effective planning.Two bivariate statistical models were employed to evaluate the landslide susceptibility: Frequency ratio (FR) and Shannon entropy (SE).
A comprehensive database of past landslides was compiled using satellite imagery and surveying the study region.In bovi-shield gold fp 5 l5 total, 581 landslide locations were identified.Two datasets containing 407 landslides (70% of the total) for model development and 174 landslides (30%) for validation were randomly selected from the total number of landslides using GIS.Slope, aspect, soil, elevation, land use and telemarkskongen flue land cover, drainage density, normalized difference vegetation index, geomorphology, road, lineament density and rainfall patterns were the 11 landslide conditioning elements considered for the modeling process.
Validation of the FR and SE models using the receiver operating characteristic curve yielded prediction accuracies of 81.6 and 82%, respectively.The SE model was the most realistic and reliable for landslide identification and prediction, followed by the FR model.The derived LSMs for the area can enhance decision-making in landslide management and guide strategic planning for the Kundah taluk in the Nilgiris, ultimately helping to prevent future landslide events.