National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making

doi:10.3390/ijgi9060393

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所

	National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making
	Moharrami Meisam 1; Naboureh Amin 2,3; Nachappa Thimmaiah Gudiyangada 4; Ghorbanzadeh Omid 4; Guan Xudong 3; Blaschke Thomas 4
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2020
卷号	9 期号:6 页码:393
关键词	spatial decision support system landslide FAHP FBWM natural hazards eastern Alps Austria
DOI	10.3390/ijgi9060393
通讯作者	Ghorbanzadeh, Omid(omid.ghorbanzadeh@stud.sbg.ac.at)
产权排序	3
文献子类	Article;Early Access
英文摘要	Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level.
电子版国际标准刊号	2220-9964
资助项目	Austrian Science Fund (FWF) through the GIScience Doctoral College[DKW1237-N23] ; Austrian Science Fund (FWF)
WOS关键词	LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; LAND-USE ; GIS ; PROBABILITY ; ZONATION ; HAZARD ; MODEL ; MAPS ; TREE
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000553573600001
资助机构	Austrian Science Fund (FWF) through the GIScience Doctoral College ; Austrian Science Fund (FWF)
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/35347]
专题	中国科学院水利部成都山地灾害与环境研究所
通讯作者	Ghorbanzadeh Omid
作者单位	1.Univ Tehran, Fac Geog, Dept Remote Sensing & GIS, Tehran 1417466191, Iran;; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;; 3.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China;; 4.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria
推荐引用方式 GB/T 7714	Moharrami Meisam,Naboureh Amin,Nachappa Thimmaiah Gudiyangada,et al. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2020,9(6):393.
APA	Moharrami Meisam,Naboureh Amin,Nachappa Thimmaiah Gudiyangada,Ghorbanzadeh Omid,Guan Xudong,&Blaschke Thomas.(2020).National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,9(6),393.
MLA	Moharrami Meisam,et al."National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 9.6(2020):393.