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Prospects of Artificial Intelligence in the Environment

Research output: Book/ReportBook - authoredpeer-review

Abstract

This book gives readers insight into the state-of-the-art use of artificial intelligence for the environment. It encompasses most of the significant facets of current breakthroughs in the fields of conceptions, methodologies, resources, and leading artificial intelligence solutions for the environment. This book presents research at the forefront on applications of artificial intelligence in combating climate change, natural hazards, and textile dyeing pollution (water pollution), for forecasting, assessing air quality trends, and air pollution monitoring. It explains how machine learning can prove to be an efficient technique to forecast the consumption of energy and how AI can be effective for renewable energy systems. Research in this book widens its scope to present the problems, opportunities, and directives for the application of AI systems in engine exhaust prediction. One of the new and interesting things explored is to provide and predict the rate of decay of human lung tissue (due to Particulate Matter exposure) with the help of AI in this book. Likewise, the book opens its scope to various environmental problems and focuses on giving the best solutions with an application of artificial intelligence; this feature makes this book an indispensable guide for environmental scientists and AI researchers of all levels. The book is written comprehensively so that engineering professionals, programmers, environmentalists, graduates, postgraduates, and researchers from beginning/intermediate level to advance level can be enlightened.

Original languageEnglish
PublisherSpringer, Singapore
Number of pages226
Edition1
ISBN (Electronic)9789819668632
ISBN (Print)9789819668632, 9789819668656
DOIs
Publication statusPublished - 17 Jun 2025

Bibliographical note

Publisher Copyright:
© 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

  • pollution
  • machine learning
  • waste management
  • climate change
  • natural hazards
  • particulate matter
  • artificial intelligence
  • environment
  • mathematical modeling
  • Environmental sustainability
  • air quality

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