Department of Computer Science and Engineering, School of Computing, Mohan Babu University, (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh, India.
Air pollution causes about seven million pre mature deaths globally every year, making it a critical issue that requires urgent attention. The key to mitigating its devastating effects lies in understanding its nature, identifying sources and trends, and predicting its. Accurate Real-time air pollution forecasting is a challenging task due to its spatiotemporal dynamics, requiring sophisticated modeling approaches. In our study, employed the Sequential Array-based Convolutional LSTM (SACLSTM) framework, which captures spatial and temporal correlations by integrating deep CNNs for spatial analysis with deep LSTM models for temporal prediction. To further enhance the model's accuracy, optimized the SACLSTM parameters using the Quantum-based Draft Mongoose Optimization Algorithm (QDMOA). Using ten days of nitrogen dioxide (NO₂) data from Los Angeles County, developed a sequential encoder-decoder network capable of predicting air pollution levels ten days into the future. By reformatting satellite air quality images into a 5D tensor, achieved precise predictions of nitrogen dioxide concentrations across various locations and time periods in Los Angeles. Our results are thoroughly documented with metrics and visualizations, clearly demonstrating the factors behind the improved accuracy. The comparison of results highlights the effectiveness of our approach in providing reliable air pollution forecasts.
Keywords
Air pollution, Convolutional Neural Networks, Quantum Based Draf Mongoose Optimization Algorithm, Long Short-Term Memory, Los Angeles.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Sivakumaran AR, Cuddapah Anitha, Manjula Arunraj, Ebinezer M D J and Gokila S;
Methodology: Sivakumaran AR, Cuddapah Anitha and Manjula Arunraj;
Software: Ebinezer M D J, Venkatesh Babu S and Gokila S;
Data Curation: Cuddapah Anitha, Manjula Arunraj and Ebinezer M D J;
Writing- Original Draft Preparation: Sivakumaran AR, Ebinezer M D J, Venkatesh Babu S and Gokila S;
Visualization: Ebinezer M D J, Venkatesh Babu S and Gokila S;
Supervision: Ebinezer M D J, Venkatesh Babu S and Gokila S;
Writing- Reviewing and Editing: Sivakumaran AR, Cuddapah Anitha and Manjula Arunraj;
All authors reviewed the results and approved the final version of the manuscript.
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Sivakumaran AR
Department of Information Technology, Malla Reddy Engineering College for Women, Secunderbad, Telangana, India.
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Cite this article
Sivakumaran AR, Cuddapah Anitha, Manjula Arunraj, Ebinezer M.D.J, Venkatesh Babu S and Gokila S, “Mitigating Air Pollution Risks with Deep Learning: A Quantum-Optimized Approach for Nitrogen Dioxide Prediction in Los Angeles”, Journal of Machine and Computing, pp. 709-719, April 2025, doi: 10.53759/7669/jmc202505056.
