Artificial Intelligence and Machine Learning in Smart City Planning
- 1st Edition - January 11, 2023
- Imprint: Elsevier
- Editors: Vedik Basetti, Chandan Kumar Shiva, Mohan Rao Ungarala, Shriram S. Rangarajan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 5 0 3 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 5 0 4 - 7
Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in var… Read more
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Request a sales quoteArtificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques.
- Reviews the smart city concept and teaches how it can contribute to achieving urban development priorities
- Explains soft computing techniques for smart city applications
- Describes how to model problems for effective analysis, intelligent decision making, and optimal operation and control in the smart city paradigm
- Teaches how to carry out independent projects using soft computing techniques in a vast range of areas in diverse fields like engineering, management, and sciences
Researchers and grad students; Practitioners in smart city planning/design
- Cover
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter One: A study on the perceptions of officials on their duties and responsibilities at various levels of the organizational structure in order to accomplish artificial intelligence-based smart city implementation
- Abstract
- 1: Introduction
- 2: Smart city assessment
- 3: Challenges in SCM
- 4: Stakeholders involved in SCM
- 5: Duties and responsibilities of the officials in executing the AI
- 6: Importance of roles of stakeholders in implementing SCM
- 7: Conclusion
- References
- Part One: Smart city framework and implementation
- Chapter Two: Integration of IoT with big data analytics for the development of smart society
- Abstract
- 1: Introduction
- 2: Key terminology of IoT and big data
- 3: Standards and protocols of IoT
- 4: Data analytics for IoT
- 5: IoT-based big data analytics platform
- 6: Challenges and issues of IoT and data analytics
- 7: Conclusions and future directions
- References
- Chapter Three: Deep learning model for flood estimate and relief management system using hybrid algorithm
- Abstract
- 1: Introduction
- 2: Literature survey
- 3: Flood detection system design
- 4: Conclusion and future work
- References
- Further reading
- Chapter Four: Powering data-driven decision-making for the development of urban economies in India
- Abstract
- 1: Overview
- 2: Literature review
- 3: AI/ML in local economy: Problem statement
- 4: LEIP: Introduction and methodology
- 5: Envisioning AI/ML in LEIP and future of local economic planning
- Annexure: Indicators used for index building under rapid economic assessment, scoring and data sources
- References
- Chapter Five: An investigation into the effectiveness of smart city projects by identifying the framework for measuring performance
- Abstract
- 1: Introduction
- 2: Measuring the effectiveness of smart cities
- 3: About measurement concept
- 4: Performance measurement and its dimensions
- 5: Performance effectiveness and its dimensions: Product measures
- 6: Measurement models used in industries
- 7: Designing a framework for smart city performance measurement
- 8: Conclusion
- References
- Part Two: Smart water management
- Chapter Six: Waste water-based pico-hydro power for automatic street light control through IOT-based sensors in smart cities: A pecuniary assessment
- Abstract
- 1: Introduction
- 2: System description
- 3: IOT devices for automatic light control
- 4: Conclusion and result analysis
- References
- Part Three: Smart education
- Chapter Seven: Reigniting the power of artificial intelligence in education sector for the educators and students competence
- Abstract
- 1: Introduction
- 2: Significance of the study
- 3: Need of the study
- 4: Objectives of the study
- 5: Scope of the study
- 6: Review of literature
- 7: Research methodology
- 8: Theoretical framework
- 9: Analysis of artificial intelligence in education sector
- 10: Conclusion
- References
- Chapter Eight: A study of postgraduate students’ perceptions of key components in ICCC to be used in artificial intelligence-based smart cities
- Abstract
- 1: Introduction
- 2: Integration of Command and Control Center
- 3: Need for ICCC assessment
- 4: MoHUA livability index at smart cities
- 5: Current process of implementation of ICCC
- 6: Architecture of ICCC
- 7: The ICCC’s command and control layer is in charge of managing
- 8: ICCC maturity assessment framework
- 9: Maturity assessment process
- 10: Evaluation criteria: ICCC functional capability assessment
- 11: ICCC functional capability assessment
- 12: Technology assessment
- 13: Governance assessment
- 14: ICCC maturity ranking
- 15: On-site maturity assessment
- 16: Importance of ICCC security
- 17: Reasons of increasing the securing of ICCC
- 18: Conclusion
- Reference
- Part Four: Smart environment
- Chapter Nine: Renewable energy based hybrid power quality compensator based on deep learning network for smart cities
- Abstract
- 1: Introduction
- 2: CIGRE LV multifeeder microgrid: Analysis of power quality issues
- 3: Renewable energy-based hybrid power quality compensator with CIGRE LV multifeeder microgrid
- 4: Conclusion
- References
- Chapter Ten: Predicting subgrade and subbase California bearing ratio (CBR) failure at Calabar-Itu highway using AI (GP, ANN, and EPR) techniques for effective maintenance
- Abstract
- 1: Overview
- 2: AI/ML in highway pavement subgrade and subbase construction and maintenance
- 3: Application of AI/ML in subgrade and subbase CBR
- 4: Recent developments
- 5: Summary
- References
- Further reading
- Chapter Eleven: Machine learning algorithms-based solar power forecasting in smart cities
- Abstract
- 1: Introduction
- 2: Overview of machine learning
- 3: Methodology
- 4: Results and analysis
- 5: Conclusion
- References
- Chapter Twelve: Smart grid: Solid-state transformer and load forecasting techniques using artificial intelligence
- Abstract
- 1: Introduction
- 2: Power distribution system
- 3: Load forecasting
- 4: Summary
- References
- Chapter Thirteen: Machine learning and predictive control-based energy management system for smart buildings
- Abstract
- 1: Introduction: Smart cities and smart buildings
- 2: Energy management system for a smart building
- 3: Predictive control-based EMS design for BIMGs
- 4: Smart homes
- 5: Application of machine learning
- 6: Future trends and research challenges in smart building
- References
- Chapter Fourteen: Effective prediction of solar energy using a machine learning technique
- Abstract
- 1: Introduction
- 2: Significance of this estimate
- 3: Research technique
- 4: Results
- 5: Conclusion
- References
- Chapter Fifteen: Experience in using sensitivity analysis and ANN for predicting the reinforced stone columns’ bearing capacity sited in soft clays
- Abstract
- 1: Introduction
- 2: Background
- 3: ANNs “artificial neural networks”
- 4: Results and discussions
- 5: Conclusions
- References
- Chapter Sixteen: Sensitivity analysis and estimation of improved unsaturated soil plasticity index using SVM, M5P, and random forest regression
- Abstract
- 1: Introduction
- 2: Background
- 3: Soft computing techniques
- 4: Conclusions
- References
- Chapter Seventeen: Forecasting off-grid solar power generation using case-based reasoning algorithm for a small-scale system
- Abstract
- 1: Overview
- 2: Literature review
- 3: AI/ML in forecasting and imputation of missing values
- 4: Results and discussion
- 5: Applications of CBR in forecasting and imputation of missing values
- 6: Summary
- References
- Chapter Eighteen: Implementing an ANN model and relative importance for predicting the under drained shear strength of fine-grained soil
- Abstract
- 1: Introduction
- 2: Background
- 3: ANN “artificial neural networks”
- 4: Data collection
- 5: Performance indicators and developed ANNs model
- 6: Equivalent equation of ANN model
- 7: Results and discussions
- 8: Relative importance
- 9: Conclusions
- References
- Part Five: Smart transportation
- Chapter Nineteen: Smart transportation based on AI and ML technology
- Abstract
- 1: Introduction
- 2: Review of related literature
- 3: The built environment’s role in creating an efficient and competitive city
- 4: Current challenges in mobility and transportation systems
- 5: ML is being used in battery research and development
- 6: Artificial intelligence (AI) role in electric vehicle (EV) and smart grid integration
- 7: Smart warehouse logistics and supply chain management
- 8: Conclusions and future directions are summarized in this section
- References
- Further reading
- Part Six: Tackling cyber attacks
- Chapter Twenty: Generative adversarial network-based deep learning technique for smart grid data security
- Abstract
- 1: Introduction
- 2: Problem formulation and design considerations
- 3: Proposed methodology
- 4: Result and discussion
- 5: Conclusion
- References
- Part Seven: Smart communications
- Chapter Twenty-one: An overview of smart city planning—The future technology
- Abstract
- 1: Introduction
- 2: Approach to artificial intelligence, machine learning, and deep learning for smart city planning
- 3: Overview of smart city
- 4: Cybersecurity in smart city planning
- 5: Conclusion
- References
- Index
- Edition: 1
- Published: January 11, 2023
- Imprint: Elsevier
- No. of pages: 360
- Language: English
- Paperback ISBN: 9780323995030
- eBook ISBN: 9780323995047
VB
Vedik Basetti
Vedik Basetti earned his PhD degree from the National Institute of Technology, Hamirpur, India, in 2016. Dr. B. Vedik joined the Department of Electrical and Electronics Engineering, SR University, Warangal, T.S., India, as faculty in 2016, where presently he is working as Associate Professor. His research work has been published in various reputed international journals and international conferences. His research interest includes soft computing techniques, smart grid, smart transmission system, and power system state estimation. Dr. B. Vedik is an IEEE member and a professional member of ACM.
Affiliations and expertise
Department of Electrical and Electronics Engineering, SR University, Warangal, IndiaCS
Chandan Kumar Shiva
Chandan Kumar Shiva has completed his BTech in electrical and electronics engineering from Ran Vijay Singh College of Engineering & Technology, Jamshedpur, India. He holds a PhD from the Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India. At present, he is Assistant Professor in the Department of Electrical and Electronics Engineering at SR University, Warangal, India. His field of interest comprises artificial intelligent techniques, smart grid, automatic generation control, and power system optimization.
Affiliations and expertise
Department of Electrical and Electronics Engineering, SR University, Warangal, IndiaMU
Mohan Rao Ungarala
Mohan Rao Ungarala is a lecturer in the Department of Applied Sciences at Université du Québec à Chicoutimi (UQAC), Québec, Canada. Since 2018, he is also a postdoctoral researcher at UQAC with the Research Chair on the Aging of Power Network Infrastructure. Dr. Mohan is a senior member of the IEEE and a member of the IEEE DEIS. Since 2019, he is an active member of the IEEE DEIS Technical Committee on “Liquid Dielectrics”, and led an IEEE International Study Group on “Pre-breakdown in Ester Liquids”. His main research interests include aging phenomena of high-voltage insulation, condition monitoring of electrical apparatus, alternative dielectric materials, transformer insulation in cold countries, and AIML applications.
Affiliations and expertise
Department of Applied Sciences (DSA), Universite du Quebec a Chicoutimi (UQAC), Quebec, CanadaSR
Shriram S. Rangarajan
Shriram S. Rangarajan is Associate Professor at SR University, Warangal, India. His industrial and R&D experience includes working as Test Engineer at M.S. Kennedy Corporation in New York, Research Associate and Planning Engineer at London Hydro Inc. in Canada, Global Research Consultant at General Electric in Bangalore, and Research Assistant at Duke Energy eGrid—Clemson University Restoration Institute in United States.
Affiliations and expertise
Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, IndiaRead Artificial Intelligence and Machine Learning in Smart City Planning on ScienceDirect