气候变化领域集成服务门户(CCPortal): Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

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DOI	10.1007/s11069-021-04743-4
	Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey
	Akinci H.; Zeybek M.
发表日期	2021
ISSN	0921030X
起始页码	1515
结束页码	1543
卷号	108 期号:2
英文摘要	Landslide susceptibility maps provide crucial information that helps local authorities, public institutions, and land-use planners make the correct decisions when they are managing landslide-prone areas. In recent years, machine-learning techniques have become very popular for producing landslide susceptibility maps. This study aims to compare the performance of these machine learning models with the traditional statistical methods used to produce landslide susceptibility maps. The landslide susceptibility for Ardanuc, Turkey was evaluated using three models: logistic regression (LR), support vector machine (SVM), and random forest (RF). Ten parameters that are effective in landslide occurrence are used in this study. The accuracy and prediction capabilities of the models were assessed using both the receiver operating characteristic (ROC) curve and area under the curve (AUC) methods. According to the AUC method, the success rate of the LR, SVM, and RF models was 83.1%, 93.2%, and 98.3%, respectively. Further, the prediction rates were calculated as 82.9% (LR), 92.8% (SVM), and 97.7% (RF). According to the verification results, RF and SVM models outperformed the traditional LR model in terms of success and prediction rate. The RF model, however, performed better than the SVM model in terms of success and prediction rates. The landslide susceptibility maps produced as a result of this study can guide city planners, local administrators, and public institutions related to disaster management to prevent and reduce landslide hazards. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	GIS Landslide susceptibility assessment Logistic regression Random forest Support vector machine
英文关键词	Meleagris gallopavo
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/206070
作者单位	Faculty of Engineering, Dept. of Geomatics Engineering, Artvin Coruh University, Artvin, 08100, Turkey; Güneysınır Vocational School, Selcuk University, Güneysınır, Konya, Turkey
推荐引用方式 GB/T 7714	Akinci H.,Zeybek M.. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey[J],2021,108(2).
APA	Akinci H.,&Zeybek M..(2021).Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey.Natural Hazards,108(2).
MLA	Akinci H.,et al."Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey".Natural Hazards 108.2(2021).