Landslide Susceptibility Mapping Using Bivariate Frequency Ratio Model and Geospatial Techniques: A Case from Karbi Angl
The study attempts to prepare an inventory map of landslide susceptibility using geospatial technology and bivariate frequency ratio model for Karbi Anglong West district in Assam, India. Past landslide locations were extracted from the landslide hazard z
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Abstract The study attempts to prepare an inventory map of landslide susceptibility using geospatial technology and bivariate frequency ratio model for Karbi Anglong West district in Assam, India. Past landslide locations were extracted from the landslide hazard zonation map of Assam for preparing landslide susceptibility. Of the total past landslide locations, 70% locations were utilized for building the model and 30% locations for validating landslide susceptibility map. Geology, lineament, slope, aspect, drainage, land use/land cover, and soil conditioning parameters were integrated through frequency ratio model to prepare the susceptibility map. High and moderate susceptibililty areas were found in the south and south-western parts having steep slopes, while low susceptibility areas were distributed sparsely over areas having gentle slope in the district. Validation of landslide susceptibility map revealed its accordance with the past landslide locations. The accuracy of the landslide susceptibility map was assessed through receiver operating characteristics curves. Prediction rate and success rate under curves were found to be 0.884 and 0.854, respectively. The map produced through the integration of landslide causative factors and frequency ratio model helped not only in identifying landslide-prone areas but also proved to be instrumental for analyzing level of susceptibility. Thus, the methodology can be employed for monitoring and assessing landslide susceptibility. Keywords Landslide susceptibility · Landslide-conditioning factors · Frequency Ratio · Geospatial techniques
R. Ahmed · R. Singh · H. Sajjad (*) Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, Delhi, India © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. Kumar et al. (eds.), Remote Sensing and GIScience, https://doi.org/10.1007/978-3-030-55092-9_4
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1 Introduction Landslides can occur in many forms including debris flows, rock falls, rockslides, rock avalanches, soil slips, and mud-flows. Such disasters constitute about 9% of the total natural disasters occurring globally but some frequent landslides may cause destruction, devastation, and loss of life and property (Gokceoglu et al. 2005). Landslide is considered as an often occurring natural hazard in the hilly regions of India during monsoon season. These hilly tracts have been categorized into five earthquake zones by the Indian Meteorological Department where the strongest earthquake has also been found. One of the causes of triggering landslides particularly in critically exposed areas is unstable slope. Of the total geographical area of India, 15% of its landmass (0.49 million sq. km) is considered to be landslide-prone (Geological Survey of India 1998). Nearly 20% area in north-eastern India experiences frequent landslides. Hence, identification, monitoring, and assessment of landslide susceptibility is essential for reducing disaster risk in such areas. Some sch
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