Urban Computing and Artificial Intelligence - Khan, Ansar; Santamouris, Mattheos; Niyogi, Dev; (ed.) - Prospero Internet Bookshop

 
Product details:

ISBN13:9780443141683
ISBN10:0443141681
Binding:Paperback
No. of pages:225 pages
Size:229x152 mm
Language:English
700
Category:

Urban Computing and Artificial Intelligence

A Data-Driven Tool for Urban Heat Mitigation
 
Publisher: Elsevier
Date of Publication:
 
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EUR 125.00
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Long description:

Urban Computing and Artificial Intelligence: A Data-Driven Tool for Urban Heat Mitigation is the first full synthesis of modern scientific and applied research on climate change, urban warming, and the future of resilient cities. The book helps city governments better understand how to plan for the effects of climate change and impending natural disasters. It compiles the concepts, strategies, and technologies associated with resilient cities, and provides an outline of what constitutes climate change and its behavior relating to urban systems. Finally, the book develops a comprehensive concept for the future resiliency of cities related to hydro-climatology and extreme events.

Next, it explains the physical principles governing the formation of distinct hydro-climatology and resilient cities, and then illustrates how this knowledge can be applied to moderate the undesirable consequences of swift and haphazard urban development (energy, peak electricity demand, health, comfort, economy, and environment) and help to create more sustainable and resilient cities for the future. With urban climate science now a fully-fledged growing field, this timely book fulfils the need to bring together the disparate parts of urban climate research in global cities into a coherent framework. It is an ideal resource for students, researchers, and policymakers in the fields of urban climate, urban architecture and planning, environmental engineering, urban design, and redevelopment.




  • Instructs on the incorporation of urban data, urban climate, and meteorological data into the design, planning, and operation of urban areas in order to make them safer, healthier, and more sustainable cities
  • Discusses solutions for a broad range of problems such as spatial and temporal variations in peak electricity demand, the impact of extreme urban heat on public health, the societal and economic costs of urban extreme urban heat, the impact of urbanization on diurnal rainfall and the environment, the impacts of adaptation measures on urban climate, and more
  • Facilitates communications with policymakers and end-users of urban data and urban meteorological and climatological data
Table of Contents:
1. Weather extremes and urban warming: Are urban digital twins approaches emerging as the ultimate tool for resilient cities
2. The use of urban digital twins through machine learning and artificial intelligence in the design, planning, and making resilient cities
3. Urban warming and global energy crisis: how to achieve sustainable energy for future cities through machine learning and artificial intelligence
4. Seasonal variations in peak electricity demand in response to urban warming: Application of Google earth engine and artificial intelligence
5. Urban climate change and public health risk: deploying artificial intelligence for human health adaptation to urban warming in cities
6. Urban heat and human thermal comfort: developing health data, impacts, and indices through machine learning and artificial intelligence
7. Economic and societal costs of urban warming: understanding compound economic impact of climate change through machine learning algorithms
8. Urbanization and urban warming: deployment of urban digital twins to study the impacts on diurnal rainfall modification
9. Urban warming, energy balance and thermal management: the case studies for cost-effective and operative in urban heat mitigation through artificial intelligence
10. The impact of urban pollution in cities: an ensemble machine learning model for accurate air pollution detection
11. Major outcomes and limitations in future research and innovation agenda for using machine learning and artificial intelligence in urban digital twins for urban climate research