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Hybrid Models for Environmental Monitoring by Integrating Deep Belief Networks With Sensor Networks
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Author(s): S. David Samuel Azariya (Sona College of Technology, India), J. Jeba Emilyn (Sona College of Technology, India), V. Sathiyamoorthi (Government Polytechnic College, Dharmapuri, India), Sengathir Janakiraman (CVR College of Engineering, India)and V. Vijayagopal (Dr. M.G.R. Educational and Research Institute, India)
Copyright: 2025
Pages: 22
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.ch012
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Abstract
Understanding our world's condition and human activity's effects on ecosystems depends on environmental monitoring. This chapter examines how sensor network integration with Deep Belief Networks (DBNs) might improve environmental monitoring. The intricacy and noise of ecological data provide challenges for traditional approaches, but real-time information from sensor networks is invaluable. DBNs are perfect for deciphering sensor data because they extract intricate patterns from high-dimensional data. Environmental monitoring is now more precise and flexible thanks to this connection. Case studies in climate change forecasting and air and water quality monitoring demonstrate the useful advantages. The chapter highlights the promise of merging deep learning and environmental monitoring for sustainable management while addressing integration issues and outlining future developments.
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