Artificial intelligence and machine learning in smart city planning / edited by Vedik Basetti [and three others].

Format:

Book

Contributor:

Basetti, Vedik, editor.

Language:

English

Subjects (All):

Artificial intelligence.

City planning--Data processing.

City planning.

Smart cities--Data processing.

Smart cities.

Physical Description:

1 online resource (362 pages)

Place of Publication:

Amsterdam, Netherlands ; Oxford, England ; Cambridge, Massachusetts : Elsevier, [2023]

Summary:

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 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.

Contents:

Intro

Artificial Intelligence and Machine Learning in Smart City Planning

Copyright

Contents

Contributors

Chapter One: A study on the perceptions of officials on their duties and responsibilities at various levels of the organi ...

1. Introduction

1.1. Smart Cities Mission (SCM)

2. Smart city assessment

3. Challenges in SCM

4. Stakeholders involved in SCM

4.1. Involvement of stakeholders in smart cities

5. Duties and responsibilities of the officials in executing the AI

6. Importance of roles of stakeholders in implementing SCM

Local governments and municipal governments

Institutions of finance and investors

Nationals/citizens

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

2. Key terminology of IoT and big data

2.1. Need of IoT

2.2. Use of big data for faster computations

3. Standards and protocols of IoT

4. Data analytics for IoT

4.1. Characteristics of IoT-generated data

4.2. Big data analytics life cycle

4.3. Types of data analytics technologies for IoT

5. IoT-based big data analytics platform

5.1. Requirements

5.2. Proposed IoT-based big data analytics pipeline

5.3. Comparison with existing platforms

6. Challenges and issues of IoT and data analytics

7. Conclusions and future directions

Chapter Three: Deep learning model for flood estimate and relief management system using hybrid algorithm

2. Literature survey

3. Flood detection system design

3.1. Important steps involved in flood management

3.1.1. Identify flood periods

3.1.2. Cluster flood sequences

3.1.3. Rainfall feature extraction

3.1.4. Flood alert

4. Conclusion and future work.

References

Further reading

Chapter Four: Powering data-driven decision-making for the development of urban economies in India

1. Overview

2. Literature review

2.1. Best practices and the complexity discourse

2.2. Factors in the local economy

2.3. Economic clusters and agglomeration

3. AI/ML in local economy: Problem statement

4. LEIP: Introduction and methodology

4.1. Rapid economic assessment

4.2. Economic competency

4.3. Cluster analysis

4.4. Decision support system

5. Envisioning AI/ML in LEIP and future of local economic planning

5.1. Tool architecture

Annexure: Indicators used for index building under rapid economic assessment, scoring and data sources

Chapter 4. References

Chapter Five: An investigation into the effectiveness of smart city projects by identifying the framework for measuring p ...

2. Measuring the effectiveness of smart cities

3. About measurement concept

The reasons for designing frameworks using indicators are such as:

4. Performance measurement and its dimensions

5. Performance effectiveness and its dimensions: Product measures

5.1. Process measures

5.2. People measures

5.3. Policy measures

5.4. Place measures

6. Measurement models used in industries

7. Designing a framework for smart city performance measurement

8. Conclusion

Part Two: Smart water management

Chapter Six: Waste water-based pico-hydro power for automatic street light control through IOT-based sensors in smart cit ...

2. System description

2.1. Waste water collection analysis

2.2. Sewage water treatment

2.3. Pico-hydropower generation

2.4. BES system design and analysis

3. IOT devices for automatic light control.

3.1. Circuit connections

3.2. Working

4. Conclusion and result analysis

Part Three: Smart education

Chapter Seven: Reigniting the power of artificial intelligence in education sector for the educators and students competence

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

7.1. Types of data

7.2. Research method

7.3. Limitations of the study

8. Theoretical framework

9. Analysis of artificial intelligence in education sector

10. Conclusion

Chapter Eight: A study of postgraduate students perceptions of key components in ICCC to be used in artificial intelligen ...

2. Integration of Command and Control Center

The purposes of utilization of ICCC

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 ICCCs 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

2. CIGRE LV multifeeder microgrid: Analysis of power quality issues

2.1. Case 1: Without compensation

2.2. Custom power devices

2.3. Custom power park

2.4. Deep learning network.

3. Renewable energy-based hybrid power quality compensator with CIGRE LV multifeeder microgrid

3.1. Working of deep neural network used in ReHPQC in CIGRE multifeeder microgrid

3.2. Scaled conjugate gradient backpropagation

3.3. Case 2-With compensation using ReHPQC

4. Conclusion

Chapter Ten: Predicting subgrade and subbase California bearing ratio (CBR) failure at Calabar-Itu highway using AI (GP, ...

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

4.1. Statistical analysis of the database

4.2. Research program

4.3. Preliminary studies

4.4. GP prediction of California bearing ratio (CBR)

4.5. ANN prediction of California bearing ratio (CBR)

4.6. EPR prediction of California bearing ratio (CBR)

5. Summary

Chapter Eleven: Machine learning algorithms-based solar power forecasting in smart cities

2. Overview of machine learning

3. Methodology

3.1. Data collection and data preprocessing for accurate energy prediction

3.1.1. Data preprocessing

Data preprocessing techniques

3.1.2. Building the model

3.1.3. Training and testing the model

3.1.4. Machine learning models

Mean-absolute error (MAE)

Mean-absolute percentage error (MAPE)

Root mean-square error (RMSE)

4. Results and analysis

5. Conclusion

Chapter Twelve: Smart grid: Solid-state transformer and load forecasting techniques using artificial intelligence

2. Power distribution system

2.1. Future power distribution system in smart city

2.1.1. Solid-state transformer

2.1.2. Various enhanced features of SST

2.1.3. Different Configurations of SST

3. Load forecasting.

3.1. Statistical approach

3.2. Artificial intelligence-based technique

4. Summary

Chapter Thirteen: Machine learning and predictive control-based energy management system for smart buildings

1. Introduction: Smart cities and smart buildings

2. Energy management system for a smart building

2.1. Building-integrated microgrids

3. Predictive control-based EMS design for BIMGs

3.1. Fundamentals on model predictive control (MPC)

3.2. Developing models of BIMGs

4. Smart homes

4.1. Basics of FPGA

4.2. Role of FPGA is developing IoT for smart homes

4.3. IoTs for home energy management (HEM) using FPGA

5. Application of machine learning

5.1. Brief description on artificial intelligence (AI) and machine learning (ML)

5.2. Application of ML on the EMS of a smart building

5.2.1. Objective function

5.2.2. Important system constraints

5.2.3. Renewable energy and load data prediction

5.2.4. Methodology used for optimization

5.2.5. Simulation results

6. Future trends and research challenges in smart building

Chapter Fourteen: Effective prediction of solar energy using a machine learning technique

2. Significance of this estimate

3. Research technique

3.1. Object and roof segmentation

3.2. Calculating the azimuth and pitch of a roof

3.3. Increase the quantity of solar panels

3.4. Calculating solar potential

4. Results

Chapter Fifteen: Experience in using sensitivity analysis and ANN for predicting the reinforced stone columns bearing cap ...

2. Background

3. ANNs ``artificial neural networks´´

3.1. ANN structure selection

3.2. Data collection

3.3. Performance measures

4. Results and discussions

4.1. Model equation and sensitivity analysis

5. Conclusions.

References.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Other Format:

Print version: Basetti, Vedik Artificial Intelligence and Machine Learning in Smart City Planning

ISBN:

9780323995047

0323995047