Abstract
ABSTRACTLandslides are common and frequent occurring phenomenon in hilly terrain during monsoon season. The primary objectives of the research work are to carry out a comprehensive analysis by quantifying the landslide susceptibility using an integrated approach of random forest (RF) with the probabilistic likelihood ratio (RF-PLR), fuzzy logic (FL) and index of entropy (IOE) in Gangtok city of Sikkim state, India. Landslide inventories are prepared based on LISS-IV (MX) satellite imagery, Google Earth and reported data of Geological Survey of India. Altogether 12 landslide conditioning factors viz. slope, elevation, curvature, aspect, land use/land cover, geology, lineament, rainfall, soil type, soil thickness, water regime and distance from road are considered as input data for geospatial modelling of landslide susceptibility. Finally, model-derived landslide susceptibility maps are classified into four hazard zones, i.e. low, medium, high and very high. To measure model compatibility model comparison is performed in ArcGIS environment and models performance is assessed by confusion matrix where RF-FL gives more accuracy of 69.36% than other two models with 9.68% and 19.35% of Type I and type II error, respectively. The outputs are validated using success and prediction rate method where, RF-PLR, RF-FL and RF-IOE show area under curve (AUC) of success and prediction rate as 76%, 67%, 83%, 78% and 85%, 80%, respectively. Additionally, the differences in model performances were analyzed by means of Wilcoxon signed rank test, where it was found that statistically differences in the performance was significant in case of RF-PLR vs. RF-FL and RF-PLR vs. RF-IOE.
Highlights
Sikkim Himalaya faces numerous landslides per year resulting in thousands of fatalities (Bhasin et al 2002)
The enhancement from single statistics to hybrid techniques is checked by success and prediction rate curve
random forest (RF)-Probabilistic likelihood ratio (PLR) model shows type-I error and type-II error of 13% and 23.38%, respectively with overall accuracy of 55.64%, while RF-fuzzy logic (FL) model shows the overall agreement of 69.36% with 9.68% and 19.35% of type-I error and type-II error respectively
Summary
Sikkim Himalaya faces numerous landslides per year resulting in thousands of fatalities (Bhasin et al 2002). Landslide susceptibility mapping is very important for disaster prevention and mitigation guide. These maps can be quantitative or qualitative (Soeters and van Westen 1996; Guzzetti et al 1999). In the last few decades, quantitative methods which are based on the relationship between landslide occurrence and controlling factors become popular (van Westen, Rengers, and Soeters 2003, 2008; Glade and Crozier 2005; Chen and Wang 2014; Kannan, Saranathan, and Anbalagan 2015; Samia et al.2018). Landslide susceptibility is a time-invariant concept that defines the probability of landslide occurrence in an area based on a set of controlling factors, i.e. geology, slope, land use land cover, etc. Landslide susceptibility is dissimilar from landslide hazard because landslide hazard considers the temporal probability of landslide occurrences and its magnitude (Guzzetti et al 2005)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
