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

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