气候变化领域集成服务门户(CCPortal): Assessment of susceptibility to landslides through geographic information systems and the logistic regression model

CCPortal

DOI	10.1007/s11069-020-03997-8
	Assessment of susceptibility to landslides through geographic information systems and the logistic regression model
	Riegel R.P.; Alves D.D.; Schmidt B.C.; de Oliveira G.G.; Haetinger C.; Osório D.M.M.; Rodrigues M.A.S.; de Quevedo D.M.
发表日期	2020
ISSN	0921030X
起始页码	497
结束页码	511
卷号	103 期号:1
英文摘要	The increase in the frequency of natural disasters in recent years and its consequent social, economic and environmental impacts make it possible to prioritize areas of risk as an essential measure in order to maximize harm reduction. This case study, developed in the city of Novo Hamburgo, Rio Grande do Sul state, Brazil, aims to identify and evaluate areas susceptible to mass movements, through the development of a model based on logistic regression, associated to Geographic Information System (GIS). The construction of the model was based on the use of only four independent variables (slope, geological aspects, pedological aspects and land use and coverage) and a binary variable, which refers to the occurrence of mass movements. In total, 123,308 pixels were used as samples for the logistic regression modeling in SPSS software. As a result, we have the spatialization of a mass movement probability map with 87.3% of the correctly sorted pixels. A validation with the landslide susceptibility map built by the Brazilian Geological Survey was also performed using the receiver operating characteristic (ROC) curve, indicating a prediction accuracy of 82.5%. This research showed the efficiency of the integrated use of GIS and logistic regression, with emphasis on the relative simplicity of the model, speed of application and good ability to identify areas susceptible to landslides. The proposed model allowed the determination of the probability of occurrence of landslides with good predictive capacity, surpassing the usual model used by the Geological Survey of Brazil (CPRM). © 2020, Springer Nature B.V.
关键词	Geoprocessing Landslides Logistic regression Natural disasters
英文关键词	efficiency measurement; geological survey; GIS; hazard assessment; identification method; landslide; logistics; mass movement; model test; model validation; pixel; probability; regression analysis; slope dynamics; spatial analysis; Brazil; Rio Grande do Sul
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/205902
作者单位	Feevale University, ERS 239, 2755, Novo Hamburgo, Rio Grande Do Sul 93525-075, Brazil; Federal University of Rio Grande Do Sul, Av. Bento Gonçalves, 9500 (prédio 44202), Porto Alegre, RS 90501-970, Brazil; Vale do Taquari University – Univates, Av. Avelino Tallini, 171, Lajeado, RS 95914-014, Brazil
推荐引用方式 GB/T 7714	Riegel R.P.,Alves D.D.,Schmidt B.C.,et al. Assessment of susceptibility to landslides through geographic information systems and the logistic regression model[J],2020,103(1).
APA	Riegel R.P..,Alves D.D..,Schmidt B.C..,de Oliveira G.G..,Haetinger C..,...&de Quevedo D.M..(2020).Assessment of susceptibility to landslides through geographic information systems and the logistic regression model.Natural Hazards,103(1).
MLA	Riegel R.P.,et al."Assessment of susceptibility to landslides through geographic information systems and the logistic regression model".Natural Hazards 103.1(2020).