LEADER 04418nam 22006495 450 001 996465443603316 005 20200701153258.0 010 $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$b[electronic resource] /$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 $a996465443603316 996 $aSmart Cities: Big Data Prediction Methods and Applications$92257589 997 $aUNISA