气候变化领域集成服务门户(CCPortal): Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

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DOI	10.1007/s11069-021-04638-4
	Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data
	Eiras C.G.S.; Souza J.R.G.; Freitas R.D.A.; Barella C.F.; Pereira T.M.
发表日期	2021
ISSN	0921030X
起始页码	1427
结束页码	1442
卷号	107 期号:2
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.
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
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206206
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
推荐引用方式 GB/T 7714	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],2021,107(2).
APA	Eiras C.G.S.,Souza J.R.G.,Freitas R.D.A.,Barella C.F.,&Pereira T.M..(2021).Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data.Natural Hazards,107(2).
MLA	Eiras C.G.S.,et al."Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data".Natural Hazards 107.2(2021).