Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods
Xi'an University of Science and Technology · Ministry of Natural Resources · +9 more institutions
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Abstract
No abstract available for this paper.
Citation impact
481
total citations
- FWCI
- 27.73
- Percentile
- 100%
- References
- 92
Citations per year
Authors
11- WCWei Chen
Xi'an University of Science and Technology, Ministry of Natural Resources, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation
- YLYang Li
Xi'an University of Science and Technology
- WXWeifeng Xue
Xi'an University of Science and Technology, Shaanxi Coal Chemical Industry Technology Research Institute
- HSHiman Shahabi
University of Kurdistan
- SLShaojun Li
Institute of Rock and Soil Mechanics
Topics & keywords
Topics
Keywords
- Flood myth
- Random forest
- Statistics
- Bayes' theorem
- Decision tree
- Tree (set theory)
- Naive Bayes classifier
- Computer science
UN Sustainable Development Goals
- Climate action
No related works found for this paper.
Funding
- NNNational Natural Science Foundation of ChinaAwards: U1765206, 41807192
- CAChinese Academy of SciencesAward: 115242KYSB20170022
- CPChina Postdoctoral Science FoundationAwards: 2017M613168, 2018T111084
- INIran National Science FoundationAward: 96004000
- SPShaanxi Province Postdoctoral Science FoundationAward: 2017BSHYDZZ07