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008 200325s2020 si | s |||| 0|eng d
020 _a9789811528378
_9978-981-15-2837-8
024 7 _a10.1007/978-981-15-2837-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLiu, Hui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aSmart Cities: Big Data Prediction Methods and Applications
_h[electronic resource] /
_cby Hui Liu.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aXXXV, 314 p. 251 illus., 20 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart 1 Exordium -- 1. Key Issues of Smart Cities -- Part 2 Smart Grid and Buildings -- 2. Electrical Characteristics and Correlation Analysis in Smart Grid -- 3. Prediction Model of City Electricity Consumption -- 4. Prediction Models of Energy Consumption in Smart Urban Buildings -- Part 3 Smart Traffic Systems -- 5. Characteristics and Analysis of Urban Traffic Flow in Smart Traffic Systems -- 6. Prediction Model of Traffic Flow Driven Based on Single Data in Smart Traffic Systems -- 7. Prediction Models of Traffic Flow Driven Based on Multi-dimensional Data in Smart Traffic Systems -- Part 4 Smart Environment 8 Prediction Models of Urban Air Quality in Smart Environment -- 9. Prediction Models of Urban Hydrological Status in Smart Environment -- 10. Prediction Model of Urban Environmental Noise in Smart Environment.
520 _aSmart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques. This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities, and explores three key areas that can be improved using big data prediction: grid energy, road traffic networks and environmental health in smart cities. The big data prediction methods proposed in this book are highly significant in terms of the planning, construction, management, control and development of green and smart cities. Including numerous case studies to explain each method and model, this easy-to-understand book appeals to scientists, engineers, college students, postgraduates, teachers and managers from various fields of artificial intelligence,smart cities, smart grid, intelligent traffic systems, intelligent environments and big data computing.
650 0 _aArtificial intelligence.
650 0 _aBig data.
650 0 _aComputational intelligence.
650 0 _aArchitecture.
650 0 _aNeural networks (Computer science) .
650 1 4 _aArtificial Intelligence.
650 2 4 _aBig Data.
650 2 4 _aComputational Intelligence.
650 2 4 _aCities, Countries, Regions.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811528361
776 0 8 _iPrinted edition:
_z9789811528385
776 0 8 _iPrinted edition:
_z9789811528392
856 4 0 _uhttps://doi.org/10.1007/978-981-15-2837-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175694
_d175694