Abstract
This research work presents a comparative performance of geographic information system (GIS)-based statistical models for landslide susceptibility mapping (LSM) of the Himalayan watershed in India. A total of 190 landslide locations covering an area of 14.63 km2 were identified in the watershed, using high-resolution linear imaging self-scanning (LISS IV) data. The causative factors used for LSM of the study area are slope, aspect, lithology, curvature, lineament density, land cover and drainage buffer. The spatial database has been prepared using remote sensing data along with ancillary data like geological maps. LSMs were prepared using information value (InV), frequency ratio (FR) and analytical hierarchy process (AHP) models. The validation results using the prediction rate curve technique show 89.61%, 87.12% and 88.26% area under curve values for FR, AHP and InV models, respectively. Therefore, the frequency ratio (FR) model could be used for LSM in other parts of this hilly terrain.
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Acknowledgements
Authors are grateful to the Department of Science and Technology (DST) for providing a research grant under project no. NRDMS/11/3023/013(G) for carrying out the studies. The authors also gratefully acknowledge the administrative and logistic support provided by the respective head of the Institutions. The suggestions from the reviewers for improvement of this research work is highly acknowledged by the authors.
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Sharma, S., Mahajan, A.K. A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull Eng Geol Environ 78, 2431–2448 (2019). https://doi.org/10.1007/s10064-018-1259-9
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DOI: https://doi.org/10.1007/s10064-018-1259-9