National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making
文献类型：期刊论文
| 作者 | Moharrami Meisam1; Naboureh Amin2,4; Nachappa Thimmaiah Gudiyangada3; Ghorbanzadeh Omid3; Guan Xudong2; Blaschke Thomas3 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 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 |
| 产权排序 | 3 |
| 通讯作者 | Ghorbanzadeh, Omid(omid.ghorbanzadeh@stud.sbg.ac.at) |
| 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 |
| WOS关键词 | LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; LAND-USE ; GIS ; PROBABILITY ; ZONATION ; HAZARD ; MODEL ; MAPS ; TREE |
| 资助项目 | Austrian Science Fund (FWF) through the GIScience Doctoral College[DKW1237-N23] ; Austrian Science Fund (FWF) |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000553573600001 |
| 出版者 | MDPI |
| 资助机构 | Austrian Science Fund (FWF) through the GIScience Doctoral College ; Austrian Science Fund (FWF) |
| 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.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China;; 3.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;; |
| 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. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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