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Volumn 2016, Issue , 2016, Pages

Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; SOCIAL NETWORKING (ONLINE);

EID: 84991384176     PISSN: 16875265     EISSN: 16875273     Source Type: Journal    
DOI: 10.1155/2016/3264587     Document Type: Article
Times cited : (22)

References (25)
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    • Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging
    • Z. Yin, J. Yin, S. Xu, and J. Wen, "Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging, " Journal of Geographical Sciences, vol. 21, no. 2, pp. 274-284, 2011.
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    • Yin, Z.1    Yin, J.2    Xu, S.3    Wen, J.4
  • 6
    • 84877659064 scopus 로고    scopus 로고
    • Exposure assessment of rainstorm waterlogging on buildings in central urban area of Shanghai based on scenario simulation
    • R.-S. Quan, M. Liu, L.-J. Zhang et al., "Exposure assessment of rainstorm waterlogging on buildings in central urban area of shanghai based on scenario simulation, " Chinese Geographical Science, vol. 2, pp. 148-152, 2011.
    • (2011) Chinese Geographical Science , vol.2 , pp. 148-152
    • Quan, R.-S.1    Liu, M.2    Zhang, L.-J.3
  • 8
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    • Application of imitated ecosystems in urban waterlogging prevention
    • Trans Tech Publications
    • S. G. Wu, M. T. Zhou, and D. D. Ma, "Application of imitated ecosystems in urban waterlogging prevention, " in Applied Mechanics and Materials, vol. 587-589, pp. 501-504, Trans Tech Publications, 2014.
    • (2014) Applied Mechanics and Materials , vol.587-589 , pp. 501-504
    • Wu, S.G.1    Zhou, M.T.2    Ma, D.D.3
  • 21
    • 84907018053 scopus 로고    scopus 로고
    • Transfer learning with multiple sources via consensus regularized autoencoders
    • T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, Eds. vol 8726of LectureNotes in Computer Science
    • F. Zhuang, X. Cheng, S. J. Pan, W. Yu, Q. He, andZ. Shi, "Transfer learning with multiple sources via consensus regularized autoencoders, " inMachine Learning andKnowledgeDiscovery in Databases, T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, Eds., vol. 8726of LectureNotes in Computer Science, pp. 417-431, 2014.
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  • 24
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    • Appearance models based on kernel canonical correlation analysis
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    • Melzer, T.1    Reiter, M.2    Bischof, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.