國立虎尾科技大學 |

Smart Cities: Big Data Prediction Methods and Applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Smart Cities: Big Data Prediction Methods and Applications/ by Hui Liu.
作者:	Liu, Hui.
面頁冊數:	XXXV, 314 p. 251 illus., 20 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-981-15-2837-8
ISBN:	9789811528378

Smart Cities: Big Data Prediction Methods and Applications
Liu, Hui.

Smart Cities: Big Data Prediction Methods and Applications[electronic resource] /by Hui Liu. - 1st ed. 2020. - XXXV, 314 p. 251 illus., 20 illus. in color.online resource.

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.

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, 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.

ISBN: 9789811528378

Standard No.: 10.1007/978-981-15-2837-8doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Smart Cities: Big Data Prediction Methods and Applications
LDR:03224nam a22003975i 4500 001 1023524
003 DE-He213
005 20200701153258.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811528378 $9 978-981-15-2837-8
024 7 $a 10.1007/978-981-15-2837-8 $2 doi
035 $a 978-981-15-2837-8
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Liu, Hui. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1063190
245 1 0 $a Smart Cities: Big Data Prediction Methods and Applications $h [electronic resource] / $c by Hui Liu.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XXXV, 314 p. 251 illus., 20 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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, 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 $a Artificial intelligence. $3 559380
650 0 $a Big data. $3 981821
650 0 $a Computational intelligence. $3 568984
650 0 $a Architecture. $3 555123
650 0 $a Neural networks (Computer science) . $3 1253765
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Cities, Countries, Regions. $3 676394
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811528361
776 0 8 $i Printed edition: $z 9789811528385
776 0 8 $i Printed edition: $z 9789811528392
856 4 0 $u https://doi.org/10.1007/978-981-15-2837-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)