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Reservoir risk modelling using a hybrid approach based on the feature selection technique and ensemble methods
Xiong, Junnan1,2; Pang, Quan1; Cheng, Weiming2,3,4,6; Wang, Nan2,3; Yong, Zhiwei5
2020-11-23
Source PublicationGEOCARTO INTERNATIONAL
ISSN1010-6049
Pages25
Corresponding AuthorCheng, Weiming(chengwm@lreis.ac.cn)
AbstractFlash 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.
KeywordFlash flood reservoir risk J48 Decision Tree genetic algorithm Bagging random forest China
DOI10.1080/10106049.2020.1852615
Indexed BySCI
Language英语
Funding ProjectStrategic 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]
Funding OrganizationStrategic 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
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000617243400001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/136115
Collection中国科学院地理科学与资源研究所
Corresponding AuthorCheng, Weiming
Affiliation1.Southwest Petr Univ, Sch Civil Engn & Geomat, Chengdu, Peoples R China
2.Chinese Acad Sci, CAS, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.Univ Chinese Acad Sci, 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
Recommended Citation
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.
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