Comprehensive spatial analysis landslide susceptibility modelling, spatial cluster analysis and priority zoning for environment analysis
Author:
Funder
Universitas Negeri Malang
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13762-024-05950-9.pdf
Reference105 articles.
1. Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front 11(3):871–883. https://doi.org/10.1016/j.gsf.2019.10.001
2. Alimohammadlou Y, Najafi A, Yalcin A (2013) Landslide process and impacts: a proposed classification method. CATENA 104:219–232. https://doi.org/10.1016/j.catena.2012.11.013
3. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003
4. Arnáez J, Lana-Renault N, Lasanta T, Ruiz-Flaño P, Castroviejo J (2015) Effects of farming terraces on hydrological and geomorphological processes. A review. CATENA 128:122–134. https://doi.org/10.1016/j.catena.2015.01.021
5. Aytun C, Erdogan S, Pata UK, Cengiz O (2024) Associating environmental quality, human capital, financial development and technological innovation in 19 middle-income countries: a disaggregated ecological footprint approach. Technol Soc 76:102445. https://doi.org/10.1016/j.techsoc.2023.102445
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