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000930279 020__ $$a9789811528378$$q(electronic book)
000930279 020__ $$a9811528373$$q(electronic book)
000930279 020__ $$z9811528365
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000930279 035__ $$aSP(OCoLC)on1147299296
000930279 035__ $$aSP(OCoLC)1147299296
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000930279 049__ $$aISEA
000930279 050_4 $$aTD159.4
000930279 08204 $$a307.760285$$223
000930279 1001_ $$aLiu, Hui,$$eauthor.
000930279 24510 $$aSmart cities :$$bbig data prediction methods and applications /$$cHui Liu.
000930279 264_1 $$aSingapore :$$bSpringer,$$c[2020]
000930279 264_4 $$c©2020
000930279 300__ $$a1 online resource :$$billustrations
000930279 336__ $$atext$$btxt$$2rdacontent
000930279 337__ $$acomputer$$bc$$2rdamedia
000930279 338__ $$aonline resource$$bcr$$2rdacarrier
000930279 504__ $$aIncludes bibliographical references.
000930279 5050_ $$aPart 1 Exordium -- 1. Key Issues of Smart Cities -- Part 2 Smart Grid and Buildings -- 2. Electrical Characteristics and Correlation Analysis in Smart Grid -- 3. Prediction Model of City Electricity Consumption -- 4. Prediction Models of Energy Consumption in Smart Urban Buildings -- Part 3 Smart Traffic Systems -- 5. Characteristics and Analysis of Urban Traffic Flow in Smart Traffic Systems -- 6. Prediction Model of Traffic Flow Driven Based on Single Data in Smart Traffic Systems -- 7. Prediction Models of Traffic Flow Driven Based on Multi-dimensional Data in Smart Traffic Systems -- Part 4 Smart Environment 8 Prediction Models of Urban Air Quality in Smart Environment -- 9. Prediction Models of Urban Hydrological Status in Smart Environment -- 10. Prediction Model of Urban Environmental Noise in Smart Environment.
000930279 506__ $$aAccess limited to authorized users.
000930279 520__ $$aSmart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques. This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities, and explores three key areas that can be improved using big data prediction: grid energy, road traffic networks and environmental health in smart cities. The big data prediction methods proposed in this book are highly significant in terms of the planning, construction, management, control and development of green and smart cities. Including numerous case studies to explain each method and model, this easy-to-understand book appeals to scientists, engineers, college students, postgraduates, teachers and managers from various fields of artificial intelligence, smart cities, smart grid, intelligent traffic systems, intelligent environments and big data computing.
000930279 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 9, 2020).
000930279 650_0 $$aSmart cities.
000930279 650_0 $$aSmart cities$$xForecasting.
000930279 650_0 $$aSmart cities$$xMathematical models.
000930279 77608 $$iPrint version:$$z9811528365$$z9789811528361$$w(OCoLC)1133126579
000930279 852__ $$bebk
000930279 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2837-8$$zOnline Access$$91397441.1
000930279 909CO $$ooai:library.usi.edu:930279$$pGLOBAL_SET
000930279 980__ $$aEBOOK
000930279 980__ $$aBIB
000930279 982__ $$aEbook
000930279 983__ $$aOnline
000930279 994__ $$a92$$bISE