The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning-Integrated Air Quality and Environmental Monitoring Processes
|
|
Author(s): V. Sujay (Department of Computer Science and Engineering, Krishna University College of Engineering and Technology, India), P. Siva Satya Sreedhar (Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India), Harshada Bhushan Magar (Department of Electronics and Telecommunication, AISSMS Institute of Information Technology, Pune, India), S. C. Shamkuwar (Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune, India)and T. Pravin (Research and Development, RSP Science Hub, Coimbatore, India)
Copyright: 2025
Pages: 28
Source title:
Enhancing Data-Driven Electronics Through IoT
Source Author(s)/Editor(s): Bhagwan Das (Centre for Artificial Intelligence Research and Optimization (AIRO), Torrens University Australia, Australia), Muhammad Zakir Shaikh (Universidad de Málaga, Spain), Samreen Hussain (Dawood University of Engineering and Technology, Pakistan)and Enrique Nava Baro (Universidad de Málaga, Spain)
DOI: 10.4018/979-8-3693-5448-3.ch014
Purchase
|
Abstract
This chapter examines the collaboration between machine learning and remote sensing technology to improve air quality and environmental monitoring. It applies advanced techniques and extensive datasets to examine, forecast, and manage environmental elements in real time. The chapter explores the significance of monitoring air quality and the environment for public health and sustainable development. It explains the fundamentals of remote sensing and machine learning and how they can join forces. This research delves into how satellite imagery, sensor networks, and data fusion methods can provide a comprehensive view of the environment. It investigates their successful applications in various regions with diverse climates. The chapter highlights the challenges in this field such as obtaining quality data, addressing intensive computational requirements, and fostering interdisciplinary collaboration. It also outlines future directions and emerging opportunities, with a focus on promising technologies like deep learning and cloud computing.
Related Content
|
P. V. Naveen, A. Poongodi.
© 2026.
24 pages.
|
|
Sathya Selvaraj Sinnasamy, S. Kamaleswari, U. Surendar, Biswaranjan Senapati, B. Vaidianathan, M. Gandhi.
© 2026.
14 pages.
|
|
B. Aarthi, A. Smruthi, Pamireddy Thanishka, G. Sakthi Prasanna, P. Mahendran.
© 2026.
18 pages.
|
|
R. Radhika, A. Muthukumaravel.
© 2026.
24 pages.
|
|
R. Regin, K. Lalith Reddy, R. Sanjay Narayanan, Y. Likhith Srinivas, R. Steffi, S. Saranya, S. R. Saranya.
© 2026.
26 pages.
|
|
R. Saranya, S. Silvia Priscila.
© 2026.
20 pages.
|
|
Manjunath Singh H., R. Tanuja.
© 2026.
28 pages.
|
|
|