Prediction of Air Quality Index using SVR Machine Learning Model and Anlayzing Hazards Arising from Thermal Power Plants

Authors

  • Paneri Fulbandhe Department of CSE, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Monali Gulhane Department of CSE, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Sudhanshu Maurya Department of CSE, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
  • Amit Pimpalwar Department of Computer Science and Engineering (AIML), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India
  • Nitin Rakesh Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

Keywords:

Contactless attendance tracking, Automated system, Face detection and identification, Machine Learning, Performance analysis, distance and confidence level correlation

Abstract

Thermal power plants, while necessary for energy generation, pose significant risks to the environment and public health. This study examines the effects of many factors, such as greenhouse gas emissions and dangerous pollutants, on air pollution. It also investigates the influence of water withdrawal, temperature discharge, and contamination on water pollution. Additionally, it explores the consequences of land degradation and waste creation. Furthermore, it investigates the correlated health ramifications, including respiratory ailments, cardiovascular disorders, and cancer. The research reveals the capacity for machine learning to alleviate these risks and foster a more sustainable power future. Machine learning may be utilized to forecast and optimize emissions, boost environmental surveillance, enable the integration of renewable energy, and optimize waste management. The study finishes by emphasizing the ethical obligation to responsibly develop new technologies, giving priority to openness, eliminating any biases, and ensuring that social and environmental advantages are of utmost importance.

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References

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Published

29.01.2024

How to Cite

Fulbandhe, P. ., Gulhane, M. ., Maurya, S. ., Pimpalwar, A. ., & Rakesh, N. . (2024). Prediction of Air Quality Index using SVR Machine Learning Model and Anlayzing Hazards Arising from Thermal Power Plants. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 603–608. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4625

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

License

Copyright (c) 2024 Paneri Fulbandhe, Monali Gulhane, Sudhanshu Maurya, Amit Pimpalwar, Nitin Rakesh

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