Sensor collected intelligence

Sensor collected intelligence: current trends and advances in computer-aided intelligent environmental data engineering

by Ighalo, Joshua O. Marques, Gonçalo

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Title Statements

  • Full title :Sensor collected intelligence : current trends and advances in computer-aided intelligent environmental data engineering / edited by Goncalo Marques, Joshua O. Ighalo.

Edition Statements

  • Publication Date:2022.

Publication Statements

  • Publisher(s):Academic Press
  • Place of Publication:Amsterdam
  • Language:eng
  • Publication Date:2022.

Notes

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

The Physical Object

  • Description:1 online resource : : illustrations (black and white, and colour).

Edition Identifiers

  • ISBN:9780323855983, 0323855989
  • oclc:on1314337888
  • Dewey Decimal:628.028563

Subjects

Artificial intelligence -- Engineering … Computer engineering Environmental engineering -- Data proce…

Writers

Ighalo, Joshua O. Marques, Gonçalo

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