气候变化领域集成服务门户(CCPortal): Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques

CCPortal

DOI	10.1007/s11069-021-04732-7
	Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques
	Pourghasemi H.R.; Sadhasivam N.; Amiri M.; Eskandari S.; Santosh M.
发表日期	2021
ISSN	0921030X
起始页码	1291
结束页码	1316
卷号	108 期号:1
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.
关键词	Boosted regression tree Functional discriminant analysis Generalized linear model Landslides Mixture discriminant analysis Partial least square
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/206498
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
推荐引用方式 GB/T 7714	Pourghasemi H.R.,Sadhasivam N.,Amiri M.,et al. Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques[J],2021,108(1).
APA	Pourghasemi H.R.,Sadhasivam N.,Amiri M.,Eskandari S.,&Santosh M..(2021).Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques.Natural Hazards,108(1).
MLA	Pourghasemi H.R.,et al."Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques".Natural Hazards 108.1(2021).