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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 118
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| Paper Title: | DEEP LEARNING MODEL TO DETECT AND LOCALIZE THE AIR POLLUTION USING SPATIOTEMPORAL DATA |
| Authors Name: | M.Sivasakthi , M.Sathya , V.Adhithyan , V.Jeevaprakash |
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IJRTI_204203
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| Published Paper Id: | IJRTI2505251 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Air pollution caused by the release of toxic gases from novel chemical industries has emerged as a severe threat to human health and environmental sustainability, particularly in densely populated urban areas worldwide. Addressing this escalating crisis requires effective strategies for measuring, predicting, and mitigating air quality challenges. However, traditional air quality prediction methods, such as time series analysis and conventional machine learning models, often struggle to accurately capture the intricate non-linear patterns inherent in air pollution data, including PM2.5 concentrations. To address these challenges, this project proposes a cutting edge multi-point deep learning framework based on Convolutional Long Short-Term Memory (ConvLSTM) networks for dynamic air quality forecasting. By integrating the strengths of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, ConvLSTM effectively captures both spatial and temporal data features, delivering highly accurate predictions of air quality metrics. The model is designed to forecast outputs from multiple monitoring nodes simultaneously within a unified framework, ensuring scalability and efficiency. The system includes real-time monitoring of toxic gas emissions from chemical industries, with automated alerts sent to the Pollution Control Board for immediate action. A cloud-based server manages and analyses the collected data, offering a centralized interface for detailed review. Integration with Google Map API provides intuitive visualization of pollution traces, empowering decision-makers with actionable location-based insights. By combining advanced deep learning techniques with real-time monitoring and regulatory alerts, this solution enhances air quality management and contributes to safeguarding public health and the environment. |
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| Cite Article: | "DEEP LEARNING MODEL TO DETECT AND LOCALIZE THE AIR POLLUTION USING SPATIOTEMPORAL DATA", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c440-c450, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505251.pdf |
| Downloads: | 000468 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2505251
Registration ID:204203
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c440-c450 Country: Salem, Tamilnadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505251 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505251 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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