Research on landslide hazard assessment in data-deficient areas: a case study of Tumen City, China
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01057-w.pdf
Reference42 articles.
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2. Azimi SR, Nikraz H, Yazdani-Chamzini A (2018) landslide risk assessment by using a new combination model based on a fuzzy inference system method. KSCE J Civ Eng 22(11):4263–4271. https://doi.org/10.1007/s12205-018-0041-7
3. Biçer ÇT, Ercanoglu M (2020) A semi-quantitative landslide risk assessment of central Kahramanmaraş City in the Eastern Mediterranean region of Turkey. Arab J Geosci 13(15):732. https://doi.org/10.1007/s12517-020-05697-w
4. Biswajeet P, Saro L (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
5. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6
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