globalchange  > 气候变化与战略
DOI: 10.1007/s11069-021-04732-7
论文题名:
Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques
作者: Pourghasemi H.R.; Sadhasivam N.; Amiri M.; Eskandari S.; Santosh M.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 108, 期:1
起始页码: 1291
结束页码: 1316
语种: 英语
中文关键词: Boosted regression tree ; Functional discriminant analysis ; Generalized linear model ; Landslides ; Mixture discriminant analysis ; Partial least square
英文摘要: Landslides pose a serious risk to human life and the natural environment. Here, we compare machine learning algorithms including the generalized linear model (GLM), mixture discriminant analysis (MDA), boosted regression tree (BRT), and functional discriminant analysis (FDA) to evaluate the landslide exposure regions in Fars Province, comprising an area of approximately 7% of Iran. Initially, an aggregate of 179 historical landslide occurrences was prepared and partitioned. Subsequently, ten landslide conditioning factors (LCFs) were generated. The partial least squares algorithm was utilized to assess the significance of the LCFs with the help of a training dataset which indicated that distance from road had the maximum significance in forecasting landslides, followed by altitude (Al), lithological units, and slope degree. Finally, the LSMs generated using BRT, GLM, MDA, and FDA were validated and compared using cut-off reliant and independent validation measures. The results of the validation metrics showed that GLM and BRT had an AUC of 0.908, while FDA and MDA had AUCs of 0.858 and 0.821, respectively. The results from our case study can be utilized to develop strategies and plans to minimize the loss of human lives and the natural environment. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Citation statistics:
被引频次[WOS]:22   [查看WOS记录]     [查看WOS中相关记录]
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169297
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


作者单位: Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, Enschede, 7514 AE, Netherlands; Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, 49189-434, Iran; Forest Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran; School of Earth Sciences and Resources, China University of Geosciences Beijing, Beijing, 100083, China; Department of Earth Sciences, University of Adelaide, Adelaide, SA 5005, Australia

Recommended Citation:
Pourghasemi H.R.,Sadhasivam N.,Amiri M.,et al. Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques[J]. Natural Hazards,2021-01-01,108(1)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Pourghasemi H.R.]'s Articles
[Sadhasivam N.]'s Articles
[Amiri M.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Pourghasemi H.R.]'s Articles
[Sadhasivam N.]'s Articles
[Amiri M.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Pourghasemi H.R.]‘s Articles
[Sadhasivam N.]‘s Articles
[Amiri M.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.