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010   $a981-15-2837-3
024 7 $a10.1007/978-981-15-2837-8
035   $a(CKB)4100000010770791
035   $a(DE-He213)978-981-15-2837-8
035   $a(MiAaPQ)EBC6145527
035   $a(PPN)243224648
035   $a(EXLCZ)994100000010770791
100   $a20200325d2020      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSmart Cities: Big Data Prediction Methods and Applications /$fby Hui Liu
205   $a1st ed. 2020.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020.
215   $a1 online resource (XXXV, 314 p. 251 illus., 20 illus. in color.) 
311   $a981-15-2836-5 
320   $aIncludes bibliographical references.
327   $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.
330   $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.
606   $aArtificial intelligence
606   $aBig data
606   $aComputational intelligence
606   $aArchitecture
606   $aNeural networks (Computer science) 
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aCities, Countries, Regions$3https://scigraph.springernature.com/ontologies/product-market-codes/K14000
606   $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100
615  0$aArtificial intelligence.
615  0$aBig data.
615  0$aComputational intelligence.
615  0$aArchitecture.
615  0$aNeural networks (Computer science) .
615 14$aArtificial Intelligence.
615 24$aBig Data.
615 24$aComputational Intelligence.
615 24$aCities, Countries, Regions.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
676   $a307.760285
700   $aLiu$b Hui$4aut$4http://id.loc.gov/vocabulary/relators/aut$0274539
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910409678203321
996   $aSmart Cities: Big Data Prediction Methods and Applications$92257589
997   $aUNINA