Effect of the Normalized Difference Vegetation Index (NDVI) on GIS-Enabled Bivariate and Multivariate Statistical Models for Landslide Susceptibility Mapping-Reference-Cited by-同舟云学术

Effect of the Normalized Difference Vegetation Index (NDVI) on GIS-Enabled Bivariate and Multivariate Statistical Models for Landslide Susceptibility Mapping

Published:2023-07-28 Issue:8 Volume:51 Page:1739-1756
ISSN:0255-660X
Container-title:Journal of the Indian Society of Remote Sensing
language:en
Short-container-title:J Indian Soc Remote Sens

Author:

Niraj K. C.,Singh Ankit,Shukla Dericks Praise^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development

Link

https://link.springer.com/content/pdf/10.1007/s12524-023-01738-5.pdf

Reference71 articles.

1. Achour, Y., & Pourghasemi, H. R. (2020). How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3), 871–883. https://doi.org/10.1016/j.gsf.2019.10.001

2. Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077–1095. https://doi.org/10.1007/s10346-014-0521-x

3. Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: Summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44. https://doi.org/10.1007/s100640050066

4. Ali, S. A., Parvin, F., Vojteková, J., Costache, R., Linh, N. T. T., Pham, Q. B., Vojtek, M., Gigović, L., Ahmad, A., & Ghorbani, M. A. (2021). GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geoscience Frontiers, 12(2), 857–876. https://doi.org/10.1016/j.gsf.2020.09.004

5. Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA, 114, 21–36. https://doi.org/10.1016/j.catena.2013.10.011

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