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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Air Quality Monitoring and Forecasting Using IoT and ML: A Survey on Methodologies and Challenges |
| Authors Name: | Samartha S , Prateek M Gummaraju , Ruchi Agarwal , Pallavi B |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197546
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| Published Paper Id: | IJNRD2306151 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Air pollution is the presence of compounds in the atmosphere that are hazardous to human beings and the health of other living organisms, or that can impair climate and materials. Chemical compounds such as carbon monoxide, ozone, nitrogen dioxide etc., are common air pollutants and they lower the quality of air. Air Quality Index (AQI) is a measure of the level of air pollution. Due to the rising levels of air pollution, there is a great need to implement effective air quality monitoring systems that gather data on the concentration of various air pollutants and provide not only the current assessments of the level of pollution, but also predict the level of pollution in the near future. This paper aims to discuss the current state of applicability and advancements in the implementation of various IoT based architectures for monitoring the levels of air pollution. We will examine the various machine learning and deep learning models that have been used to forecast AQI levels. Additionally, this paper will delve into the challenges and limitations faced during the implementation of IoT and ML for monitoring and predicting AQI. Overall, this survey paper aims to provide a comprehensive overview of the current state of IoT and Machine Learning implementation for measuring and forecasting AQI values. |
| Keywords: | Air pollution, AQI, IoT, Arduino, Machine Learning, AutoRegression, ARMA, ARIMA, Sensors |
| Cite Article: | "Air Quality Monitoring and Forecasting Using IoT and ML: A Survey on Methodologies and Challenges", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.b450-b455, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306151.pdf |
| Downloads: | 000118756 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2306151 Registration ID: 197546 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: b450-b455 Country: Bengaluru, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306151 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306151 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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