Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering

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

Contenu

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