气候变化领域集成服务门户(CCPortal): Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics

CCPortal

DOI	10.1007/s11069-021-04719-4
	Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics
	Liu Q.; Huang D.; Tang A.; Han X.
发表日期	2021
ISSN	0921030X
起始页码	1047
结束页码	1067
卷号	108 期号:1
英文摘要	Considering the increasing number of landslides due to permafrost degradation, this paper reports a performance evaluation of three classical landslide susceptibility models applied to cold regions. A landslide inventory was first constructed through a historical survey and image interpretation. Ten causative factors of landslides were then chosen based on the available data and the local environment. Multicollinearity diagnosis and factor effectiveness test were employed to perform a factor analysis. Subsequently, three evaluation models based on the frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) were established. These models were applied to obtain landslide susceptibility maps, which were then analyzed and compared. The model performance was evaluated in terms of the accuracy rate and fluctuation characteristics. The results showed no multicollinearity issue between the factors employed. The annual temperature difference and frozen depth are two indispensable factors when assessing landslide susceptibility in cold regions. A comparison between the susceptibility maps generated using the three models showed that the FR model-generated map is most in line with the principle of disaster zoning and has the highest degree of conformity with actual landslide points, followed by the maps generated using the LR and ANN models. An accuracy analysis showed that the ANN model yields the highest AUC value in the training and test states, 0.957 and 0.863, respectively; however, these values were not optimal given the fluctuation. Moreover, the fluctuation in the non-landslide data was greater than that in the landslide data. The fluctuation results revealed the drawback of the AUC value in the analysis of the model performance. In other words, the non-landslide error often covers up the landslide error. This study provides a scientific guidance for evaluating the model performance and for assessing landslide disasters in cold regions. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Fluctuation characteristics Landslide susceptibility Overestimation Permafrost degradation Predicted error
英文关键词	artificial neural network; cold region; factor analysis; hazard assessment; landslide; performance assessment; permafrost
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/206374
作者单位	School of Civil Engineering, Harbin Institute of Technology, No. 73 Huanghe Rd, Nangang District, Harbin, 150001, China; School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China
推荐引用方式 GB/T 7714	Liu Q.,Huang D.,Tang A.,et al. Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics[J],2021,108(1).
APA	Liu Q.,Huang D.,Tang A.,&Han X..(2021).Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics.Natural Hazards,108(1).
MLA	Liu Q.,et al."Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics".Natural Hazards 108.1(2021).