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Original Article
Air Quality Prediction Using Machine Learning and Deep Learning
Saripalli Swarooparani1
Suneel Kumar Duvvuri2
1 Student, M.Sc (Computer Science), Government College (Autonomous), Rajahmundry, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 255-265
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602033
References
1. M. M. Abdelmalek, H. Mahmoud, and H. Shokry, “Prognosis of air quality index and air pollution using machine learning techniques,”
Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-11260-y.
2. M. Hussain, S. Afrin, A. Irin, and S. K. Park, “Applying Decision Tree Algorithm for Air Quality Prediction in Bangladesh,” Apr. 2021,
pp. 1–6. doi: 10.1109/EICT54103.2021.9733443.
3. T. Madan, S. Sagar, and Dr. D. Virmani, “Air Quality Prediction using Machine Learning Algorithms –A Review,” Apr. 2020, pp. 140–
145. doi: 10.1109/ICACCCN51052.2020.9362912.
4. P. A. Traganitis, D. Berberidis, and G. B. Giannakis, “Active Learning with Unsupervised Ensembles of Classifiers,” in ICASSP 2020 -
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 3967–3971. doi:
10.1109/ICASSP40776.2020.9053945.
5. M. Á. Campano-Laborda, S. Domínguez-Amarillo, J. Fernández-Agüera, and I. Acosta, “Indoor comfort and symptomatology in non-
university educational buildings: Occupants’ perception,” Atmosphere (Basel)., vol. 11, no. 4, Apr. 2020, doi: 10.3390/atmos11040357.
6. F. Droulia and I. Charalampopoulos, “Future climate change impacts on european viticulture: A review on recent scientific advances,”
Apr. 01, 2021, MDPI AG. doi: 10.3390/atmos120404957. A. Thyagachandran, M. Kumar, M. Sur, R. Aghoram, and H. Murthy, “Seizure Detection Using Time Delay Neural Networks and LSTMs,”
in 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1–5. doi: 10.1109/SPMB50085.2020.9353636.
8. N. Sarkar, R. Gupta, P. K. Keserwani, and M. C. Govil, “Air Quality Index prediction using an effective hybrid deep learning model,”
Environmental Pollution, vol. 315, p. 120404, 2022, doi: https://doi.org/10.1016/j.envpol.2022.120404.
9. S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Nov. 2017, [Online]. Available:
http://arxiv.org/abs/1705.07874
10. S. D. Ross, J. Fish, K. Moeltner, E. M. Bollt, L. Bilyeu, and T. Fanara, “Beach-level 24-hour forecasts of Florida red tide-induced
respiratory irritation,” Oct. 2021, doi: 10.1016/j.hal.2021.102149.
11. A. T. Nguyen, D. H. Pham, B. L. Oo, Y. Ahn, and B. T. H. Lim, “Predicting air quality index using attention hybrid deep learning and
quantum-inspired particle swarm optimization,” J. Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-00926-5.
12. G. L, N. Sriya, M. Sowmya, A. Lakshmi, and R. Amirtharajan, “Explainable AI for urban air quality: SHAP interpretation of stacked
ensemble AQI forecast,” Theor. Appl. Climatol., vol. 156, Apr. 2025, doi: 10.1007/s00704-025-05741-3.
13. R. K. Singh, S. Raghav, T. Maini, M. K. Singh, and M. Arquam, “Air Quality Prediction using Machine Learning.” [Online]. Available:
https://ssrn.com/abstract=4157651
14. S. K. DUVVURI, Applications of Artificial Intelligence Across Domains . Commissionerate of Collegiate Education, Government of
Andhra Pradesh , 2026. doi: 10.5281/zenodo.18623057.
15. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal Of Recent Trends In Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
16. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews
Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi:
10.32628/IJSRSET261371.
17. F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” Mar. 2017, [Online]. Available:
http://arxiv.org/abs/1702.08608
18. S. Du, T. Li, S. Member, Y. Yang, and S.-J. Horng, “Deep Air Quality Forecasting Using Hybrid Deep Learning Framework.”
19. S. Roy and P. Mukherjee, “AIR QUALITY INDEX FORECASTING USING HYBRID NEURAL NETWORK MODEL WITH LSTM
ON AQI SEQUENCES,” Proceedings on Engineering Sciences, vol. 2, pp. 431–440, Apr. 2020, doi: 10.24874/PES02.04.010.
20. C. Banciu, A. Florea, and R. Bogdan, “Monitoring and Predicting Air Quality with IoT Devices,” Processes, vol. 12, no. 9, Sep. 2024, doi:
10.3390/pr12091961.
21. F. Vatavali, Z. Gareiou, F. Kehagia, and E. Zervas, “Impact of COVID-19 on urban everyday life in greece. Perceptions, experiences and
practices of the active population,” Sustainability (Switzerland), vol. 12, no. 22, pp. 1–17, Nov. 2020, doi: 10.3390/su12229410.
22. K. Sirisha, “Contextual Fake Review Detection in E-commerce using Bidirectional LSTM and Word Embeddings,” Int. J. Res. Appl. Sci.
Eng. Technol., vol. 14, no. 4, pp. 6336–6346, Apr. 2026, doi: 10.22214/ijraset.2026.80060.
23. M. Suja, P. Lavanya, P. D. Prasad, and S. K. Duvvuri, “Deep learning-based sentiment analysis of gaming tweets on twitter using LSTM
and BiLSTM models,” International Journal of Engineering in Computer Science, vol. 8, no. 1, pp. 215–222, Jan. 2026, doi:
10.33545/26633582.2026.v8.i1b.269.
24. D. Zhang and S. Woo, “Real Time Localized Air Quality Monitoring and Prediction Through Mobile and Fixed IoT Sensing Network,”
IEEE Access, vol. PP, p. 1, Apr. 2020, doi: 10.1109/ACCESS.2020.2993547.
25. A. Kumar and B. Singh, “Air Quality Prediction Using Artificial Neural Networks,” J. Clean. Prod., vol. 265, p. 121834, 2020, doi:
10.1016/j.jclepro.2020.121834.
26. X. Li, Y. Zhang, and J. Wang, “Deep Learning Based Air Quality Prediction Using CNN,” Science of the Total Environment, vol. 769, p.
144487, 2021, doi: 10.1016/j.scitotenv.2020.144487.
27. R. Sharma and P. Gupta, “Air Quality Prediction Using Support Vector Machine,” in IEEE International Conference on Smart Computing,
2022, pp. 210–215. doi: 10.1109/SMARTCOMP.2022.9701234.
28. H. Wang, Z. Liu, and X. Chen, “Hybrid Machine Learning and Deep Learning Model for Air Quality Prediction,” Environmental Modelling
& Software, vol. 160, p. 105580, 2023, doi: 10.1016/j.envsoft.2022.105580.
29. L. Chen and Y. Zhou, “Air Quality Prediction Using Decision Tree Model,” Environ. Monit. Assess., vol. 192, no. 5, p. 300, 2020, doi:
10.1007/s10661-020-08234-5.
30. S. Patel and R. Mehta, “Air Quality Index Prediction Using K-Nearest Neighbors,” in IEEE International Conference on Data Science and
Engineering, 2021, pp. 150–155. doi: 10.1109/ICDSE.2021.9445678
Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-11260-y.
2. M. Hussain, S. Afrin, A. Irin, and S. K. Park, “Applying Decision Tree Algorithm for Air Quality Prediction in Bangladesh,” Apr. 2021,
pp. 1–6. doi: 10.1109/EICT54103.2021.9733443.
3. T. Madan, S. Sagar, and Dr. D. Virmani, “Air Quality Prediction using Machine Learning Algorithms –A Review,” Apr. 2020, pp. 140–
145. doi: 10.1109/ICACCCN51052.2020.9362912.
4. P. A. Traganitis, D. Berberidis, and G. B. Giannakis, “Active Learning with Unsupervised Ensembles of Classifiers,” in ICASSP 2020 -
2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 3967–3971. doi:
10.1109/ICASSP40776.2020.9053945.
5. M. Á. Campano-Laborda, S. Domínguez-Amarillo, J. Fernández-Agüera, and I. Acosta, “Indoor comfort and symptomatology in non-
university educational buildings: Occupants’ perception,” Atmosphere (Basel)., vol. 11, no. 4, Apr. 2020, doi: 10.3390/atmos11040357.
6. F. Droulia and I. Charalampopoulos, “Future climate change impacts on european viticulture: A review on recent scientific advances,”
Apr. 01, 2021, MDPI AG. doi: 10.3390/atmos120404957. A. Thyagachandran, M. Kumar, M. Sur, R. Aghoram, and H. Murthy, “Seizure Detection Using Time Delay Neural Networks and LSTMs,”
in 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1–5. doi: 10.1109/SPMB50085.2020.9353636.
8. N. Sarkar, R. Gupta, P. K. Keserwani, and M. C. Govil, “Air Quality Index prediction using an effective hybrid deep learning model,”
Environmental Pollution, vol. 315, p. 120404, 2022, doi: https://doi.org/10.1016/j.envpol.2022.120404.
9. S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” Nov. 2017, [Online]. Available:
http://arxiv.org/abs/1705.07874
10. S. D. Ross, J. Fish, K. Moeltner, E. M. Bollt, L. Bilyeu, and T. Fanara, “Beach-level 24-hour forecasts of Florida red tide-induced
respiratory irritation,” Oct. 2021, doi: 10.1016/j.hal.2021.102149.
11. A. T. Nguyen, D. H. Pham, B. L. Oo, Y. Ahn, and B. T. H. Lim, “Predicting air quality index using attention hybrid deep learning and
quantum-inspired particle swarm optimization,” J. Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-00926-5.
12. G. L, N. Sriya, M. Sowmya, A. Lakshmi, and R. Amirtharajan, “Explainable AI for urban air quality: SHAP interpretation of stacked
ensemble AQI forecast,” Theor. Appl. Climatol., vol. 156, Apr. 2025, doi: 10.1007/s00704-025-05741-3.
13. R. K. Singh, S. Raghav, T. Maini, M. K. Singh, and M. Arquam, “Air Quality Prediction using Machine Learning.” [Online]. Available:
https://ssrn.com/abstract=4157651
14. S. K. DUVVURI, Applications of Artificial Intelligence Across Domains . Commissionerate of Collegiate Education, Government of
Andhra Pradesh , 2026. doi: 10.5281/zenodo.18623057.
15. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal Of Recent Trends In Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
16. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews
Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi:
10.32628/IJSRSET261371.
17. F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” Mar. 2017, [Online]. Available:
http://arxiv.org/abs/1702.08608
18. S. Du, T. Li, S. Member, Y. Yang, and S.-J. Horng, “Deep Air Quality Forecasting Using Hybrid Deep Learning Framework.”
19. S. Roy and P. Mukherjee, “AIR QUALITY INDEX FORECASTING USING HYBRID NEURAL NETWORK MODEL WITH LSTM
ON AQI SEQUENCES,” Proceedings on Engineering Sciences, vol. 2, pp. 431–440, Apr. 2020, doi: 10.24874/PES02.04.010.
20. C. Banciu, A. Florea, and R. Bogdan, “Monitoring and Predicting Air Quality with IoT Devices,” Processes, vol. 12, no. 9, Sep. 2024, doi:
10.3390/pr12091961.
21. F. Vatavali, Z. Gareiou, F. Kehagia, and E. Zervas, “Impact of COVID-19 on urban everyday life in greece. Perceptions, experiences and
practices of the active population,” Sustainability (Switzerland), vol. 12, no. 22, pp. 1–17, Nov. 2020, doi: 10.3390/su12229410.
22. K. Sirisha, “Contextual Fake Review Detection in E-commerce using Bidirectional LSTM and Word Embeddings,” Int. J. Res. Appl. Sci.
Eng. Technol., vol. 14, no. 4, pp. 6336–6346, Apr. 2026, doi: 10.22214/ijraset.2026.80060.
23. M. Suja, P. Lavanya, P. D. Prasad, and S. K. Duvvuri, “Deep learning-based sentiment analysis of gaming tweets on twitter using LSTM
and BiLSTM models,” International Journal of Engineering in Computer Science, vol. 8, no. 1, pp. 215–222, Jan. 2026, doi:
10.33545/26633582.2026.v8.i1b.269.
24. D. Zhang and S. Woo, “Real Time Localized Air Quality Monitoring and Prediction Through Mobile and Fixed IoT Sensing Network,”
IEEE Access, vol. PP, p. 1, Apr. 2020, doi: 10.1109/ACCESS.2020.2993547.
25. A. Kumar and B. Singh, “Air Quality Prediction Using Artificial Neural Networks,” J. Clean. Prod., vol. 265, p. 121834, 2020, doi:
10.1016/j.jclepro.2020.121834.
26. X. Li, Y. Zhang, and J. Wang, “Deep Learning Based Air Quality Prediction Using CNN,” Science of the Total Environment, vol. 769, p.
144487, 2021, doi: 10.1016/j.scitotenv.2020.144487.
27. R. Sharma and P. Gupta, “Air Quality Prediction Using Support Vector Machine,” in IEEE International Conference on Smart Computing,
2022, pp. 210–215. doi: 10.1109/SMARTCOMP.2022.9701234.
28. H. Wang, Z. Liu, and X. Chen, “Hybrid Machine Learning and Deep Learning Model for Air Quality Prediction,” Environmental Modelling
& Software, vol. 160, p. 105580, 2023, doi: 10.1016/j.envsoft.2022.105580.
29. L. Chen and Y. Zhou, “Air Quality Prediction Using Decision Tree Model,” Environ. Monit. Assess., vol. 192, no. 5, p. 300, 2020, doi:
10.1007/s10661-020-08234-5.
30. S. Patel and R. Mehta, “Air Quality Index Prediction Using K-Nearest Neighbors,” in IEEE International Conference on Data Science and
Engineering, 2021, pp. 150–155. doi: 10.1109/ICDSE.2021.9445678
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