Franklin

Smart Cities: Big Data Prediction Methods and Applications [electronic resource] / by Hui Liu.

Author/Creator:
Liu, Hui, author., Author,
Edition:
1st ed. 2020.
Publication:
Singapore : Springer Singapore : Imprint: Springer, 2020.
Series:
Computer Science (SpringerNature-11645)
Format/Description:
Book
1 online resource (XXXV, 314 pages) : 251 illustrations, 20 illustrations in color.
Subjects:
Artificial intelligence.
Big data.
Computational intelligence.
Architecture.
Neural networks (Computer science)
Local subjects:
Artificial Intelligence.
Big Data.
Computational Intelligence.
Cities, Countries, Regions.
Mathematical Models of Cognitive Processes and Neural Networks.
System Details:
text file PDF
Summary:
Smart 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, et cetera) 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.
Contents:
Part 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.
Contributor:
SpringerLink (Online service)
Contained In:
Springer Nature eBook
Other format:
Printed edition:
Printed edition:
Printed edition:
ISBN:
978-981-15-2837-8
9789811528378
Publisher Number:
10.1007/978-981-15-2837-8 doi
Access Restriction:
Restricted for use by site license.
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