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Urban Computing and Artificial Intelligence. A Data-Driven Tool for Urban Heat Mitigation

  • Book

  • June 2025
  • Elsevier Science and Technology
  • ID: 5917465

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.

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

Authors

Ansar Khan Department of Geography, Lalbaba College, University of Calcutta, Kolkata, India. Dr. Khan is an Assistant Professor of Geography at Lalbaba College, Howrah, India. His research incorporates simulation and numerical modeling of global climatic events including urban climate and microclimatic variation using the state-of-the-art Weather Research and Forecasting (WRF) model and Regional Climate Model (RegCM). A key focus of his research is searching for appropriate mitigation strategies and technologies to decrease the overheating of tropical urban areas while also decreasing energy consumption and protecting health. He has commendable expertise in handling data and implementing models in many software platforms. His research contribution has been recognized by some leading researchers in the domain of urban climate research. Mattheos Santamouris University of New South Wales,Sydney, Australia.

Mat Santamouris is the Anita Lawrence Professor of High Performance Architecture in the University of New South Wales in Australia. He is a past a professor at the University of Athens, Greece and visiting Professor at the Cyprus Institute, Metropolitan University of London, Tokyo Polytechnic University, Bolzano University, Brunnel University and National University of Singapore. Past President of the National Center of Renewable and Energy Savings of Greece. Editor and author of 15 international books on topics related to heat island, solar energy and energy conservation in buildings published by Earthscan, Springer, etc. Guest editor of twelve special issues of various scientific journals. Scientific coordinator of many international research programs and author of almost 290 scientific papers published in peer reviewed international scientific journals. Reviewer of research projects in 15 countries including USA, UK, France, Germany, Canada, Sweden, etc. Expert in various International Research Institutions. Highly Cited researcher according to Clarivate in 2017 and 2018.

Dev Niyogi Dave P. Carelton Centennial Professor, Department of Geological Sciences, Jackson School of Geosciences, and Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Texas, USA. Dev Niyogi is Dave P. Carelton Centennial Professor in the Department of Geological Sciences at Jackson School of Geosciences, and Department of Civil, Architectural, and Environmental Engineering in the University of Texas at Austin, USA. He was the most recent chair of the American Meteorological Society (AMS) Board of Urban Environment and elected advisory board member of the International Association of Urban Climate. He is currently serving on the AMS Committee on Applied Climatology, and has previously served on AMS Committee on Agriculture and Forest Meteorology, invited member FGDC Spatial Climate Working Group, Member of the Weather Research and Forecast (WRF) model WG-14 (land surface models), and Member of the AGU Biogeochemistry meetings group/spring meeting student awards chair. He has provided invited testimonies to the National Academy study group, planning summer meetings, and Senate Working groups.