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

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

1st Edition - March 20, 2022

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  • Editors: Goncalo Marques, Joshua Ighalo
  • Paperback ISBN: 9780323855976
  • eBook ISBN: 9780323855983

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Description

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.  

Key Features

  • 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

Readership

Academics to Industrial Professionals, Graduate and Undergraduate Students in Computer Science Engineering and Environmental Science, Software Developers and Data Scientists to support their industrial activities

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • List of contributors
  • Chapter 1. An introduction to Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering
  • Abstract
  • Introduction
  • Book structure and relevant audience
  • Intelligent systems in environmental engineering research
  • Looking to the future
  • References
  • Section 1: Data-centric and intelligent systems in air quality monitoring, assessment and mitigation
  • Chapter 2. Application of deep learning and machine learning in air quality modeling
  • Abstract
  • Introduction
  • Data profiling
  • Learning from data
  • Conclusions and further thoughts
  • Acknowledgments
  • References
  • Chapter 3. Advances in data-centric intelligent systems for air quality monitoring, assessment, and control
  • Abstract
  • Introduction
  • Overview of AI-based technologies and data-centric systems for pollution control
  • References
  • Chapter 4. Intelligent systems in air pollution research: a review
  • Abstract
  • Introduction
  • The definition of atmosphere
  • The structure of the atmosphere
  • Different contaminants in the air
  • Tropospheric ozone (O3)
  • Nitrogen dioxide (NO2)
  • Particulate matter
  • Carbon monoxide and carbon dioxide
  • Sulfur dioxide
  • Review of the literature
  • A new studied case
  • Research methodology
  • The investigated city
  • Input and output parameters
  • Error-related criteria
  • Results and discussion
  • Uncertainty of different models
  • Analyzing error for smaller ranges of the input parameters
  • Conclusions
  • References
  • Chapter 5. ESTABLISH—a decision support system for monitoring the quality of air for human health
  • Abstract
  • Introduction
  • Related work
  • ESTABLISH pilot study: user requirements
  • ESTABLISH decision support system
  • Deployment, data acquisition, and integration
  • Preliminary testing of sensors
  • Data acquisition and integration from wearable devices
  • Data acquisition and integration from environmental monitoring devices
  • Visualization of the air quality index
  • ESTABLISH platform presentation
  • User guide for patients
  • User guide for therapists
  • Conclusions
  • Acknowledgments
  • References
  • Chapter 6. Indoor air pollution: a comprehensive review of public health challenges and prevention policies
  • Abstract
  • Introduction
  • Indoor air quality and public health
  • Enhanced indoor air quality and prevention strategies
  • Technologies and control policies for enhanced indoor air quality
  • Discussion
  • Conclusion
  • References
  • Section 2: Data-centric and intelligent systems in water quality monitoring, assessment and mitigation
  • Chapter 7. Data-centric intelligent systems for water quality monitoring, assessment, and control
  • Abstract
  • Introduction
  • Problems associated with numerical modeling in hydraulic transport and water quality prediction
  • Why artificial intelligence?
  • Conclusion
  • References
  • Further reading
  • Chapter 8. ANN prognostication and GA optimization of municipal solid waste leachate treatment using aluminum electrodes via electrocoagulation-flocculation method
  • Abstract
  • Introduction
  • Methodology
  • Artificial neural network modeling
  • Genetic algorithm optimization of the ECF process
  • Statistical analysis of the data
  • Calculation of electrode and electrical consumption
  • Results and discussion
  • Genetic algorithm optimization results
  • Statistical analysis results
  • Electrode and electrical power consumption during the ECF process
  • Conclusion
  • Acknowledgment
  • References
  • Chapter 9. Application of deep learning and machine learning methods in water quality modeling and prediction: a review
  • Abstract
  • Introduction
  • Deep learning and machine learning in WQ modeling and prediction
  • Overview of learning methods
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Semisupervised learning
  • Machine learning architectures used in water quality modeling and prediction
  • Artificial neural network
  • Neural networks models
  • Support vector machines
  • Decision trees
  • Deep learning architectures used in water quality modeling and prediction
  • Convolutional neural network
  • Recurrent neural network
  • Generative unsupervised models
  • Application of ML and DL models in WQ prediction of different water systems
  • Modeling and prediction of different water systems
  • Data collection
  • Input data selection
  • Data splitting
  • Data preprocessing
  • Model structure determination
  • Model training
  • Performance evaluation measures
  • Challenges facing DL and ML predictions
  • Conclusions and future prospects
  • References
  • Chapter 10. Intelligent systems in water pollution research: a review
  • Abstract
  • Introduction
  • Water standards
  • Water desalination technologies
  • Review of the literature
  • Selected case study from the literature
  • Conclusions
  • References
  • Chapter 11. A long short-term memory deep learning approach for river water temperature prediction
  • Abstract
  • Introduction
  • Materials and methods
  • Methodology
  • Results and discussion
  • Conclusions and future recommendations
  • References
  • Section 3: Data-centric and intelligent systems in land pollution research
  • Chapter 12. Machine learning and artifical intelligence application in land pollution research
  • Abstract
  • Introduction
  • Application of deep learning and machine learning methods in flow modeling of landfill leachate
  • Application of deep learning and machine learning methods in soil quality assessment and remediation
  • Establishing a nexus between nonbiodegradable waste and data-centric systems
  • Case studies of evaluations and analysis of solid waste management techniques by deep learning and machine learning methods
  • Conclusions
  • References
  • Chapter 13. Application of artificial intelligence in the mapping and measurement of soil pollution
  • Abstract
  • Introduction
  • Methodology
  • Search and selection criteria
  • Quality check and data extraction
  • Theoretical backgrounds to the different AI models
  • Artificial neural network
  • Support vector machines
  • Adaptive neurofuzzy inference system
  • Random forest
  • Gradient boosted machine
  • Bayesian machine learning
  • Hybrid models
  • Application domain of the different AI models
  • AI models in soil pollutant measurement
  • Conclusions
  • References
  • Further reading
  • Chapter 14. Artificial intelligence in the reduction and management of land pollution
  • Abstract
  • Introduction
  • The use of artificial intelligence and robotics in system modification for effective on-the-spot minimization of wastes in process industries
  • Convolutional neural network model system of waste classification
  • Support vector machine
  • Artificial intelligence-robotics pickup system
  • Artificial intelligence and robotics in waste recycling
  • Robotic recycle sorting system
  • Artificial intelligence-robotic quality assessment system
  • Reforestation for land pollution management: impact of drones and neural network
  • Land pollution management via sustainable green agriculture: use of machine learning and robotics
  • Conclusion
  • References
  • Further reading
  • Section 4: Data-centric and intelligent systems in noise pollution research and other environmental engineering issues
  • Chapter 15. Advanced soft computing techniques in modeling noise pollution health impacts
  • Abstract
  • Introduction
  • Effect of noise pollution on human health
  • Noise pollution health-impact modeling
  • Stage 1: Defining individual constructs
  • Stage 2: Developing and specifying the measurement model
  • Stage 3: Designing a study to produce empirical results
  • Stage 4: Assessing measurement model validity
  • Stage 5: Specifying the structural model
  • Stage 6: Assessing the structural model validity
  • SEM and ANFIS case studies
  • Conclusion
  • References
  • Chapter 16. Intelligent and knowledge-based waste management: smart decision-support system
  • Abstract
  • Introduction
  • Trends in exploration and production wastes in the oil and gas industry
  • Oil and gas waste management
  • Conventional waste management approach in the oil and gas industry
  • Environmental impact of oil and gas generated wastes
  • Challenges of conventional waste management systems
  • Expert system for the oil and gas waste management system
  • Gaps in a waste management expert system
  • Effective utilization of expert systems in oil and gas industry waste management
  • Conclusion
  • References
  • Chapter 17. Computer-aided modeling of solid waste conversion: case study of maize (Zea mays) residues air gasification
  • Abstract
  • Introduction
  • Methodology
  • Results and discussion
  • Conclusion
  • References
  • Chapter 18. Neural network model for biological waste management systems
  • Abstract
  • Introduction
  • Materials and methods
  • Data-driven modeling approaches
  • Artificial neural network-based predictive modeling
  • Choosing the activation function
  • Choosing the appropriate training algorithm
  • Data preprocessing and randomization
  • Data division
  • Internal parameters of the network and performance evaluation
  • Sensitivity analysis
  • Statistical analysis
  • Results and discussions
  • Artificial neural modeling of the different biological reactors
  • Predictive capability of the model for modified RBC
  • Sensitivity analysis of inputs
  • Removal of DCM in the biotrickling filter
  • Predictive capability and sensitivity of the ANN model
  • Conclusion
  • References
  • Chapter 19. The role of artificial neural networks in bioproduct development: a case of modeling and optimization studies
  • Abstract
  • Introduction
  • Bioproduct development
  • Product formulation
  • Product deformulation
  • Selected optimization tools used in bioprocess development as computational intelligence
  • Genetic algorithms
  • Fuzzy logic
  • Application of optimization tools in bioprocessing operations
  • Bioremediation
  • Biofuel production
  • Biopharmacy
  • Future developments or trends
  • Conclusion
  • References
  • Chapter 20. Modeling of grains sun drying: from theoretical methods to intelligent systems
  • Abstract
  • Introduction
  • An account of early theoretical modeling efforts
  • Intelligent systems in the modeling of grain sun drying
  • Conclusion
  • References
  • Index

Product details

  • No. of pages: 474
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: March 20, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323855976
  • eBook ISBN: 9780323855983

About the Editors

Goncalo Marques

Gonçalo Marques holds a PhD in Computer Science Engineering and is member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as Assistant Professor lecturing courses on programming, multimedia and database systems. His current research interests include Internet of Things, Enhanced Living Environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences and is also involved in several edited books projects.

Affiliations and Expertise

Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal

Joshua Ighalo

Joshua O. Ighalo obtained his Bachelors' Degree in Chemical Engineering in 2015 from the University of Benin, Nigeria. He also received a Masters' Degree in Chemical Engineering in 2019 from the University of Ilorin, Nigeria. His research interests include computer-aided modelling and optimisation of chemical process systems, biofuel production, solid waste management, and environmental pollution control. He authored or co-authored over 25 papers in journals indexed in Scopus and Web of Scienc

Affiliations and Expertise

Department of Chemical Engineering, University of Ilorin, Ilorin, Nigeria

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