Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering (eBook, ePUB)

Redaktion: Marques, Goncalo; Ighalo, Joshua O.

• Format: ePub

Produktbeschreibung

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm.

This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering.

• Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems
• Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios
• Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 474
• Erscheinungstermin: 20. März 2022
• Englisch
• ISBN-13: 9780323855983
• Artikelnr.: 63634427

Inhaltsangabe

Section I: Data-centric and intelligent systems in air quality monitoring,
assessment and mitigation
1. Application of deep learning and machine learning in air quality
modelling
2. Case study of air quality prediction by deep learning and machine
learning
3. Considerations of particle dispersion modelling with data-centric and
intelligent systems
4. Data-centric modelling of air filters, HVAC and other industrial air
quality control systems
5. A review of recent developments and applications of data-centric systems
in air quality monitoring, assessment and mitigation
Section 2: Data-centric and intelligent systems in water quality
monitoring, assessment and mitigation
6. Application of deep learning and machine learning methods in water
quality modelling and prediction
7. Case studies of surface water, groundwater and rainwater quality
prediction by data-centric and intelligent systems
8. Application of deep learning and machine learning methods in contaminant
hydrology
9. Deep learning and machine learning methods in emerging contaminants and
micro-pollutants research
10. A review of recent developments and applications of data-centric
systems in water quality monitoring, assessment and mitigation
Section 3: Data-centric and intelligent systems inland pollution research
11. Application of deep learning and machine learning methods in flow
modelling of landfill leachate
12. Case studies of evaluations and analysis of solid waste management
techniques by deep learning and machine learning methods
13. Application of deep learning and machine learning methods in soil
quality assessment and remediation
14. Establishing a nexus between non-biodegradable waste and data-centric
systems
15. A review of recent developments and applications of data-centric
systems inland pollution research
Section 4: Data-centric and intelligent systems in noise pollution research
16. Methods development for data-centric systems in noise pollution
research
17. Case studies of data-centric systems in noise pollution research
18. A review of recent developments and applications of data-centric
systems in noise pollution research