Comparison of natural breaks method and frequency ratio dividing attribute intervals for landslide susceptibility mapping
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geology,Geotechnical Engineering and Engineering Geology
Link
https://link.springer.com/content/pdf/10.1007/s10064-023-03392-0.pdf
Reference39 articles.
1. Abudikeyimu X, He S (2019) Spatial prediction on landslide vulnerability based on MIV - BP neural network. Yangtze River(in Chinese) 50:140–144. https://doi.org/10.16232/j.cnki.1001-4179.2019.11.023
2. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
3. Alsabhan AH, Singh K, Sharma A, Alam S, Pandey DD, Rahman SAS, Khursheed A, Munshi FM (2022) Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio. J King Saud Unv Sci 34:101759. https://doi.org/10.1016/j.jksus.2021.101759
4. Arabameri A, Pradhan B, Rezaei K, Lee CW (2019) Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sens 11:999. https://doi.org/10.3390/rs11090999
5. Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436. https://doi.org/10.1007/s10346-014-0550-5
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