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Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods

文献类型：期刊论文

作者Xiong, Junnan2,3; Pang, Quan2; Cheng, Weiming1,3,4,6; Wang, Nan1,3; Yong, Zhiwei5
刊名GEOCARTO INTERNATIONAL
出版日期2020-11-23
页码25
关键词Flash flood reservoir risk J48 Decision Tree genetic algorithm Bagging random forest China
ISSN号1010-6049
DOI10.1080/10106049.2020.1852615
通讯作者Cheng, Weiming(chengwm@lreis.ac.cn)
英文摘要Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs, so an accurate assessment of reservoir risk for certain areas is necessary. Therefore, the purpose of this study was to propose a novel methodological approach for reservoir risk modelling based on the feature selection method (FSM) and tree-based ensemble methods (Bagging and Random Forest [RF]). The results showed that: (1) the J48-GA based ensemble models achieved higher learning and predictive capabilities compared to conventional ensemble models without the FSM. (2) For the classification accuracy, the J48-GA-RF (96.4%) outperformed RF (96.0%), J48-GA-Bagging (93.9%) and Bagging (93.5%). And the J48-GA-RF achieved the highest prediction AUC value (0.995), an almost perfect Kappa indexes value (0.926) and the best practicality value (30.88%). (3) In particular, the results indicated that all of the models showed high performance, both in training and in the validation of a dataset. Additionally, this study could provide a reference for disaster managers, hydraulic engineers and policy makers to implement location-specific flash flood risk reduction strategies.
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; Science and Technology Project of Xizang Autonomous Region[XZ201901-GA-07] ; Southwest Petroleum University of Science and Technology Innovation Team Projects[2017CXTD09] ; National lash Flood Investigation and Evaluation Project[SHZHIWHR-57]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000617243400001
出版者TAYLOR & FRANCIS LTD
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; Science and Technology Project of Xizang Autonomous Region ; Southwest Petroleum University of Science and Technology Innovation Team Projects ; National lash Flood Investigation and Evaluation Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/136114]  
专题中国科学院地理科学与资源研究所
通讯作者Cheng, Weiming
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Southwest Petr Univ, Sch Civil Engn & Geomat, Chengdu, Peoples R China
3.Chinese Acad Sci, CAS, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Res, Nanjing, Peoples R China
5.Southwest Petr Univ, Sch Geosci & Technol, Chengdu, Peoples R China
6.Collaborat Innovat Ctr South China Sea Studies, Nanjing, Peoples R China
推荐引用方式
GB/T 7714		 Xiong, Junnan,Pang, Quan,Cheng, Weiming,et al. Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods[J]. GEOCARTO INTERNATIONAL,2020:25.
APA Xiong, Junnan,Pang, Quan,Cheng, Weiming,Wang, Nan,&Yong, Zhiwei.(2020).Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods.GEOCARTO INTERNATIONAL,25.
MLA Xiong, Junnan,et al."Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods".GEOCARTO INTERNATIONAL (2020):25.

入库方式： OAI收割

来源：地理科学与资源研究所

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