Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering
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Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering
Ighalo, Joshua O.; Marques, Goncalo
Elsevier Science & Technology
03/2022
474
Mole
Inglês
9780323855976
15 a 20 dias
970
Descrição não disponível.
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI models; Adaptive neuro-fuzzy system; Air pollution; Air quality; Air quality prediction; Air-fuel ratio; Anthropogenic; Artificial intelligence; Artificial neural network; Biochemical reactor; Biotechnology; Computational intelligence; Computer-aided algorithms; Contaminant; Control policies; Data science; Data-centric systems; Deep learning; Desalination technologies; Dichloromethane; Drying; Environment; Environmental management; Equation of state; Exploratory factor analysis; Feedback-enabled algorithms; GEP; GPR; Gibbs-free energy; Grain; Health impact of pollution; Heat transfer; Humidification-dehumidification; Indoor air quality; Industrialization; LSTM; Land pollution; Leachate; Literature review; Loading rate; MLR; Machine learning; Machine learning technology; Mass transfer; Modeling; Multieffect distillation; Multistage flash distillation; Neural networks; Noise monitoring; Noise pollution; Nonlinear modeling techniques; OSELM; Optimization; Particle dispersion; Point sources; Pollutant concentration; Pollutants; Pollution; Predictive modeling; Public health; Pyrolysis; Recycling; Reforestation; Removal efficiency; Robotics; SVR; Soil pollution; Soil pollution mapping; Solar still; Structural equation modeling; Syngas; Urbanization; Waste treatment; Wastewater management; Water quality; Water quality forecast; Water temperature; Zea mays
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
AI models; Adaptive neuro-fuzzy system; Air pollution; Air quality; Air quality prediction; Air-fuel ratio; Anthropogenic; Artificial intelligence; Artificial neural network; Biochemical reactor; Biotechnology; Computational intelligence; Computer-aided algorithms; Contaminant; Control policies; Data science; Data-centric systems; Deep learning; Desalination technologies; Dichloromethane; Drying; Environment; Environmental management; Equation of state; Exploratory factor analysis; Feedback-enabled algorithms; GEP; GPR; Gibbs-free energy; Grain; Health impact of pollution; Heat transfer; Humidification-dehumidification; Indoor air quality; Industrialization; LSTM; Land pollution; Leachate; Literature review; Loading rate; MLR; Machine learning; Machine learning technology; Mass transfer; Modeling; Multieffect distillation; Multistage flash distillation; Neural networks; Noise monitoring; Noise pollution; Nonlinear modeling techniques; OSELM; Optimization; Particle dispersion; Point sources; Pollutant concentration; Pollutants; Pollution; Predictive modeling; Public health; Pyrolysis; Recycling; Reforestation; Removal efficiency; Robotics; SVR; Soil pollution; Soil pollution mapping; Solar still; Structural equation modeling; Syngas; Urbanization; Waste treatment; Wastewater management; Water quality; Water quality forecast; Water temperature; Zea mays
