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Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction

文献类型：期刊论文

作者Ye, Tao3,4,6,7; Liu, Weihang3,4,7; Mu, Qingyang3,4,7; Zong, Shuo3; Li, Yijia3,5; Shi, Peijun1,2,3,4,7
刊名INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
出版日期2020-09-01
卷号48页码:10
ISSN号2212-4209
关键词Livestock snow disaster Vulnerability Generalized additive models Random forest Boosted regression trees
DOI10.1016/j.ijdrr.2020.101578
通讯作者Ye, Tao(yetao@bnu.edu.cn)
英文摘要Quantitative vulnerability relationships describing the susceptibility of socioeconomic losses in response to climate change are critical for natural disaster loss modeling and risk assessment. Modeling such vulnerability requires methods capable of handling complicated multi-factor, non-linear, and interactive relationships. Here, we compared the performance of generalized additive models (GAM) and random forest (RF) and boosted regression trees (BRT) in quantifying livestock vulnerability to snow disasters in the Tibetan Plateau for both explanatory and predictive purposes. Our results indicated promising explanatory power of these three modeling methods, with deviance-based R-2 up to 0.720. They consistently revealed geophysical and socioeconomic factors that contributed to higher mortality rates. Nevertheless, GAM model failed to identify the critical influence of snow depth, mainly due to its smoothing scheme when fitting models to data. They also differed in the selection of the most important socioeconomic variable to represent prevention capacity. From a predictive perspective, all three modeling methods also showed promising predictive power, yet RF had the smallest prediction error, with less number of predictors used. Therefore, the predictive version of RF may well be the best choice for use in future risk analyses, yet those of BRT and GAM can serve as an alternative if needed.
WOS关键词REGRESSION TREE ; RANDOM FOREST ; CLIMATE-CHANGE ; RISK ; CLASSIFICATION ; IMPACTS ; MANAGEMENT ; RESPONSES
资助项目National Key Research and Development Program of China[2016YFA0602404] ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK0606] ; Fund for Creative Research Groups of the National Natural Science Foundation of China[41621061]
WOS研究方向Geology ; Meteorology & Atmospheric Sciences ; Water Resources
语种英语
出版者ELSEVIER
WOS记录号WOS:000556556400014
资助机构National Key Research and Development Program of China ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; Fund for Creative Research Groups of the National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/158132]  
专题中国科学院地理科学与资源研究所
通讯作者Ye, Tao
作者单位1.Peoples Govt Qinghai Prov, Acad Plateau Sci & Sustainabil, Xining 810016, Peoples R China
2.Beijing Normal Univ, Xining 810016, Peoples R China
3.Beijing Normal Univ, Fac Geog Sci, Key Lab Environm Change & Nat Disaster,Minist Edu, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China
4.Minist Emergency Management, Acad Disaster Reduct & Emergency Management, Beijing 100875, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
6.Boston Univ, Frederick S Pardee Ctr Study Longer Range Future, Boston, MA 02215 USA
7.Minist Educ, Beijing 100875, Peoples R China
推荐引用方式
GB/T 7714		 Ye, Tao,Liu, Weihang,Mu, Qingyang,et al. Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction[J]. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION,2020,48:10.
APA Ye, Tao,Liu, Weihang,Mu, Qingyang,Zong, Shuo,Li, Yijia,&Shi, Peijun.(2020).Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction.INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION,48,10.
MLA Ye, Tao,et al."Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction".INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 48(2020):10.

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来源：地理科学与资源研究所

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