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.
1. An introduction to Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering - p. 1 2. Section 1: Data-centric and intelligent systems in air quality monitoring, assessment and mitigation - p. 4 3. Chapter 2. Application of deep learning and machine learning in air quality modeling - p. 11 4. Chapter 3. Advances in data-centric intelligent systems for air quality monitoring, assessment, and control - p. 25 5. Chapter 4. Intelligent systems in air pollution research: a review - p. 59 6. Chapter 5. ESTABLISH—a decision support system for monitoring the quality of air for human health - p. 83 7. Chapter 6. Indoor air pollution: a comprehensive review of public health challenges and prevention policies - p. 105 8. Section 2: Data-centric and intelligent systems in water quality monitoring, assessment and mitigation - p. 119 9. Chapter 7. Data-centric intelligent systems for water quality monitoring, assessment, and control - p. 129 10. Chapter 8. ANN prognostication and GA optimization of municipal solid waste leachate treatment using aluminum electrodes via electrocoagulation-flocculation method - p. 161 11. Chapter 9. Application of deep learning and machine learning methods in water quality modeling and prediction: a review - p. 185 12. Chapter 10. Intelligent systems in water pollution research: a review - p. 219 13. Chapter 11. A long short-term memory deep learning approach for river water temperature prediction - p. 243 14. Section 3: Data-centric and intelligent systems in land pollution research - p. 266 15. Chapter 12. Machine learning and artifical intelligence application in land pollution research - p. 273 16. Chapter 13. Application of artificial intelligence in the mapping and measurement of soil pollution - p. 297 17. Chapter 14. Artificial intelligence in the reduction and management of land pollution - p. 319 18. Section 4: Data-centric and intelligent systems in noise pollution research and other environmental engineering issues - p. 330 19. Chapter 15. Advanced soft computing techniques in modeling noise pollution health impacts - p. 337 20. Chapter 16. Intelligent and knowledge-based waste management: smart decision-support system - p. 353 21. Computer-aided modeling of solid waste conversion: case study of maize (Zea mays) residues air gasification - p. 381 22. Chapter 18. Neural network model for biological waste management systems - p. 393 23. Chapter 19. The role of artificial neural networks in bioproduct development: a case of modeling and optimization studies - p. 417 24. Chapter 20. Modeling of grains sun drying: from theoretical methods to intelligent systems - p. 433 25. Index - p. 443
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.
1. An introduction to Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering - p. 1 2. Section 1: Data-centric and intelligent systems in air quality monitoring, assessment and mitigation - p. 4 3. Chapter 2. Application of deep learning and machine learning in air quality modeling - p. 11 4. Chapter 3. Advances in data-centric intelligent systems for air quality monitoring, assessment, and control - p. 25 5. Chapter 4. Intelligent systems in air pollution research: a review - p. 59 6. Chapter 5. ESTABLISH—a decision support system for monitoring the quality of air for human health - p. 83 7. Chapter 6. Indoor air pollution: a comprehensive review of public health challenges and prevention policies - p. 105 8. Section 2: Data-centric and intelligent systems in water quality monitoring, assessment and mitigation - p. 119 9. Chapter 7. Data-centric intelligent systems for water quality monitoring, assessment, and control - p. 129 10. Chapter 8. ANN prognostication and GA optimization of municipal solid waste leachate treatment using aluminum electrodes via electrocoagulation-flocculation method - p. 161 11. Chapter 9. Application of deep learning and machine learning methods in water quality modeling and prediction: a review - p. 185 12. Chapter 10. Intelligent systems in water pollution research: a review - p. 219 13. Chapter 11. A long short-term memory deep learning approach for river water temperature prediction - p. 243 14. Section 3: Data-centric and intelligent systems in land pollution research - p. 266 15. Chapter 12. Machine learning and artifical intelligence application in land pollution research - p. 273 16. Chapter 13. Application of artificial intelligence in the mapping and measurement of soil pollution - p. 297 17. Chapter 14. Artificial intelligence in the reduction and management of land pollution - p. 319 18. Section 4: Data-centric and intelligent systems in noise pollution research and other environmental engineering issues - p. 330 19. Chapter 15. Advanced soft computing techniques in modeling noise pollution health impacts - p. 337 20. Chapter 16. Intelligent and knowledge-based waste management: smart decision-support system - p. 353 21. Computer-aided modeling of solid waste conversion: case study of maize (Zea mays) residues air gasification - p. 381 22. Chapter 18. Neural network model for biological waste management systems - p. 393 23. Chapter 19. The role of artificial neural networks in bioproduct development: a case of modeling and optimization studies - p. 417 24. Chapter 20. Modeling of grains sun drying: from theoretical methods to intelligent systems - p. 433 25. Index - p. 443
