Intelligent Environmental Data Monitoring for Pollution Management

Intelligent Environmental Data Monitoring for Pollution Management is centered on evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process oriented data. It may be noted that there are lots of potential applications and prototypes in this field, yet very few real life applications are known so far. This is mainly due to the fact that most of the pollution management procedures still rely on the time consuming batch processes and field study. Thus this book will usher in a new era as far as environmental pollution management is concerned. This book reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. The book will also cover the novel intelligent algorithms for the purpose of effective environmental pollution data management at par with the standards laid down by World Health Organization. Iintroduces novel intelligent techniques needed to address environmental pollution for the well-being of the global environmentOffers perspectives for design, development and commissioning of intelligent applicationsProvides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of environmental pollutionPuts forward some insights into the future generation intelligent pollution monitoring techniques INDICE: Part 1. Introduction to Batch Data Technologies for Pollution Monitoring 1. Categories of environmental hazards 2. Fundamentals of Batch processes 3. Current techniques and methods for collection, processing and abstraction of environmental data 4. Linear and Non-linear regression models Part 2. Review on Intelligent Tools and Techniques 5. Neural networking algorithms 6. Neuro-fuzzy techniques 7. Evolutionary techniques 8. Hybrid intelligent tools 9. Quantum inspired computational intelligent techniques Part 3. Data sensing application for health and well-being applications 10. Quantum inspired neural model for prediction of removal of heavy metals from industrial effluents by green synthesis 11. Novel neuro-fuzzy technique for removal of phenol and both azo and non-azo dyes from industrial effluents 12. Intelligent prediction mechanism for isolation characterization and of soil and water bacteria for arsenic and fluoride bioremediation 13. Bio-sorbent quantity optimization for removal of pesticide and insecticide from agricultural runoff water 14. Applications for estimation of quality of nanoparticle to control pathogenic bacteria and fungus. 15. Statistical prediction of nanoparticle levels for controlling vector population

• ISBN: 978-0-12-819671-7
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 380
• Fecha Publicación: 01/11/2020
• Nº Volúmenes: 1
• Idioma: Inglés