Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Leena Sri, R.; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Jenefa, J.; Thangavel, Karthikeyan Title A name given to the resource Transformative Trends in AI for Environmental Monitoring: Challenges, Applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Environmental Monitoring Using Artificial Intelligence;pp.1-28 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394270392.ch1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394270392.ch1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001367084?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001367084?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Leena Sri R., Thiagarajar College of Engineering, Madurai, India; Vetriveeran D., Thiagarajar College of Engineering, Madurai, India; Sambandam R.K., CHRIST (Deemed to be) University, Kengeri, Bangalore, India; Jenefa J., CHRIST (Deemed to be) University, Kengeri, Bangalore, India; Thangavel K., University of Technology and Applied Sciences, Al Khuwair, Muscat, Oman Description An account of the resource The integration of artificial intelligence (AI) and machine learning (ML) is reshaping environmental monitoring, responding to the escalating complexities of issues like climate change and pollution. This article presents a comprehensive overview of current trends, challenges, and applications in AI-driven environmental monitoring. While technologies like remote sensing and Internet of Things (IoT) have improved data resolution, the sheer volume necessitates AI for efficient processing. The review emphasizes the role of AI in real-time monitoring, providing timely insights critical for addressing natural disasters and pollution. Exploring various environmental monitoring verticals-air and water quality, climate change modeling, biodiversity, and disaster prediction-the article highlights AIs versatility in addressing diverse concerns. Challenges such as data quality, bias, interpretability, and privacy are examined, underlining ethical considerations in biased models impacting marginalized communities. This chapter discusses common environmental modeling methodologies, ranging from empirical to geospatial modeling, elucidating their advantages and challenges. 2025 Scrivener Publishing LLC. Subject The topic of the resource AI challenges; Artificial intelligence; deep learning models; environment monitoring and modeling; research directions Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427039-2; 978-139427036-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online