DOI: 10.1007/s11069-021-04638-4
论文题名: Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data
作者: Eiras C.G.S. ; Souza J.R.G. ; Freitas R.D.A. ; Barella C.F. ; Pereira T.M.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 107, 期: 2 起始页码: 1427
结束页码: 1442
语种: 英语
中文关键词: Discriminant analysis
; Landslide
; Ouro Preto
; Susceptibility
; Topographical factors
英文关键词: discriminant analysis
; inventory
; landslide
; mass movement
; quantitative analysis
; topographic effect
; unmanned vehicle
; urban area
; Brazil
; Minas Gerais
; Ouro Preto
英文摘要: The city of Ouro Preto, which is located in the state of Minas Gerais, Brazil, has a long history of mass movements influenced by the regional geology, geomorphology, and anthropic activities, which have resulted in harmful consequences to the population. However, most of the studies conducted in the region are qualitative and are directly dependent on the experience specialists. The aim of this study was to analyse the landslide susceptibility in the urban region of Ouro Preto quantitatively by using discriminant analysis. The landslide inventory was obtained by using unmanned aerial vehicle images and fieldwork. ArcGIS 10.6 and R 3.5.1 software were used, and the following landslide predisposing factors were considered: slope angle, slope aspect, profile curvature, and topographic wetness index (TWI). As geological and geotechnical data are still scarce in the interior of Brazil, we only used data derived from topography to determine the effectiveness of these factors for analysing landslide susceptibility. The slope angle proved to be the factor that most differentiated unstable from stable terrain, followed by TWI. The other parameters were not as effective in differentiating the stability conditions. The model efficiency was 88.6%, the specificity was 93.3%, and the sensitivity was 85.0%. Also, the prediction and success curve were used to evaluate the accuracy of the proposed landslides model, by using the area under the curve (AUC) criteria. It was shown that the AUC values 0.851 for testing and 0.838 for training indicate that the developed model provides an excellent prediction. The main contribution of this work is the demonstration of the effectiveness of using easily accessible data (derived from topography) for analysing landslide susceptibility with a multivariate statistical method. This method can contribute valuable information to urban planning efforts in cities without the need for robust data. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169005
Appears in Collections: 气候变化与战略
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作者单位: Geotechnics Center, Federal University of Ouro Preto, Ouro Preto, Brazil; Environmental Engineering Department, Federal University of Ouro Preto, Ouro Preto, Brazil; Statistics Department, Federal University of Ouro Preto, Ouro Preto, Brazil
Recommended Citation:
Eiras C.G.S.,Souza J.R.G.,Freitas R.D.A.,et al. Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data[J]. Natural Hazards,2021-01-01,107(2)