Optimizing seismic hazard inputs for co-seismic landslide susceptibility mapping: a probabilistic analysis
Author:
Funder
Department of Science and Technology India
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06517-0.pdf
Reference61 articles.
1. Abraham MT, Satyam N, Pradhan B, Alamri AM (2020) Forecasting of landslides using rainfall severity and soil wetness: a probabilistic approach for Darjeeling Himalayas. Water 12:804. https://doi.org/10.3390/w12030804
2. Abraham MT, Satyam N, Lokesh R et al (2021) Factors affecting landslide susceptibility mapping: assessing the influence of different machine learning approaches. Sampl Strateg Data Splitt Land 10:989. https://doi.org/10.3390/land10090989
3. ALOSPALSAR (2015) Dataset: ASF DAAC 2015, ALOS PALSAR_Radiometric_Terrain_Corrected_high_res; Includes Material © JAXA/METI 2007. Accessed through ASF DAAC 12 December 2021
4. Atkinson GM (2009) Earthquake time histories compatible with the 2005 National building code of Canada uniform hazard spectrum. Can J Civ Eng 36:991–1000. https://doi.org/10.1139/L09-044
5. Azarafza M, Azarafza M, Akgün H et al (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11:24112. https://doi.org/10.1038/s41598-021-03585-1
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