the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effect of the Slope Angle and Its Classification on Landslide
Abstract. The phase after the determination of the landslide area in landslide susceptibility studies is the selection of methods and parameters to be used. Approximately 1500 randomly selected publications show that it is necessary to select a parameter based on the area. Research has shown that the parameter of slope is greatly preferred. There is nearly consensus of opinion among researchers regarding the use of the parameter. The research included the definition of slope made by different researchers, the advantages and disadvantages of the use of the parameter, different classifications that are used, the formation intervals of landslides, their use together with other parameters, and its effect on the formation of landslides. Classifications were studied based on the slope values at which landslides. Generally, automatic slope classifications are used in the preparation of landslide maps. There isn’t standard in parameter maps. Therefore, there isn’t class range that is referenced when preparing slope maps.
In this study, preferred class ranges and slope values where landslides occur were determined in the literature. 40 landslides area has been selected in Turkey. These were evaluated in the slope classes determined according to the literature. The results compared with the literature were found to be compatible.
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Interactive discussion
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RC1: 'Review of the paper nhess-2020-87', Lorenzo Marchi, 13 May 2020
- AC1: 'Answer', Seda Çellek, 13 May 2020
- RC2: 'Review', Anonymous Referee #2, 29 May 2020
Interactive discussion
-
RC1: 'Review of the paper nhess-2020-87', Lorenzo Marchi, 13 May 2020
- AC1: 'Answer', Seda Çellek, 13 May 2020
- RC2: 'Review', Anonymous Referee #2, 29 May 2020
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Cited
18 citations as recorded by crossref.
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- Quantitative characterization of geomorphological and topographical features of debris-flow channels at the Alpe di Succiso mountain, Northern Apennines (Italy) M. Rashid et al. 10.1080/17445647.2024.2422549
- Analyzing landslide susceptibility, health vulnerability and risk using multi-criteria decision-making analysis in Arunachal Pradesh, India S. Rehman & A. Azhoni 10.1007/s11600-022-00943-z
- Zonal Concept: Landscape Level Parameters and Application P. Dujka & A. Kusbach 10.2478/jlecol-2023-0009
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- Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri 10.1007/s12665-024-11533-1
- Landslide susceptibility mapping using CNN models based on factor visualization and transfer learning C. Liu 10.1007/s00477-024-02859-0
- Explainable AI Integrated Feature Selection for Landslide Susceptibility Mapping Using TreeSHAP M. Inan & I. Rahman 10.1007/s42979-023-01960-5
- Remote Sensing and GIS in Landslide Management: An Example from the Kravarsko Area, Croatia L. Podolszki & I. Karlović 10.3390/rs15235519
- Landslide susceptibility mapping in parts of Aglar watershed, Lesser Himalaya based on frequency ratio method in GIS environment D. Keshri et al. 10.1007/s12040-023-02204-z
- Soil micromorphology for modeling spatial on landslide susceptibility mapping: a case study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia A. Ahmad et al. 10.1007/s11069-023-06063-1
- A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features N. Li et al. 10.3390/s24144583
- Modeling Urban Growth and the Impacts of Climate Change: The Case of Esmeraldas City, Ecuador C. Mena et al. 10.3390/su14084704
- Coupled landslide analyses through dynamic susceptibility and forecastable hazard analysis D. Francis & L. Bryson 10.1007/s11069-024-06908-3
- Assessing Landslide Susceptibility by Coupling Spatial Data Analysis and Logistic Model A. Ganga et al. 10.3390/su14148426
Seda Çellek
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