Predicting Air Quality Index (AQI) in Western Uttar Pradesh Cities Using Machine Learning Models: A Comparative Analysis

References

[1] Air pollution and disasters. (2016). Environmental Science and Engineering (Subseries: Environmental Science), 143, 325–343. https://doi.org/10.1007/978-3-319-21596-9_8

[2] Ameer, S., Shah, M. A., Khan, A., Song, H., Maple, C., Islam, S. U., & Asghar, M. N. (2019). Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities. IEEE Access, 7, 128325–128338. https://doi.org/10.1109/ACCESS.2019.2925082

[3] Ayus, I., Natarajan, N., & Gupta, D. (2023a). Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China. Asian Journal of Atmospheric Environment, 17(1). https://doi.org/10.1007/s44273-023-00005-w

[4] Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021). Air-pollution prediction in smart city, deep learning approach. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00548-1

[5] Bellinger, C., Mohomed Jabbar, M. S., Zaïane, O., & Osornio-Vargas, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. In BMC Public Health (Vol. 17, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s12889-017-4914-3

[6] C R, A., Deshmukh, C. R., D K, N., Gandhi, P., & astu, V. (2018). Detection and Prediction of Air Pollution using Machine Learning Models. International Journal of Engineering Trends and Technology, 59(4), 204–207. https://doi.org/10.14445/22315381/IJETT-V59P238

[7] Doreswamy, Harishkumar, K. S., Km, Y., & Gad, I. (2020). Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models. Procedia Computer Science, 171, 2057–2066. https://doi.org/10.1016/j.procs.2020.04.221

[8] Gul, H., & Das, B. K. (2023). The Impacts of Air Pollution on Human Health and Well-Being: A Comprehensive Review. Journal of Environmental Impact and Management Policy, 36, 1–11. https://doi.org/10.55529/jeimp.36.1.11

[9] Gupta, N. S., Mohta, Y., Heda, K., Armaan, R., Valarmathi, B., & Arulkumaran, G. (2023). Prediction of Air Quality Index Using Machine Learning Techniques: A Comparative Analysis. Journal of Environmental and Public Health, 2023, 1–26. https://doi.org/10.1155/2023/4916267

[10] Isaev, E., Ajikeev, B., Shamyrkanov, U., Kalnur, K. U., Maisalbek, K., & Sidle, R. C. (2022). Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek. Aerosol and Air Quality Research, 22(3). https://doi.org/10.4209/aaqr.210336

[11] Janarthanan, R., Partheeban, P., Somasundaram, K., & Navin Elamparithi, P. (2021). A deep learning approach for prediction of air quality index in a metropolitan city. Sustainable Cities and Society, 67. https://doi.org/10.1016/j.scs.2021.102720

[12] Kumar, K., & Pande, B. P. (2023). Air pollution prediction with machine learning: a case study of Indian cities. International Journal of Environmental Science and Technology, 20(5), 5333–5348. https://doi.org/10.1007/s13762-022-04241-5

[13] Kumar Patel, P., Kumar Singh, H., & Hrishikesh Kumar Singh, E. (2260). A MACHINE LEARNING-BASED APPROACH TO PREDICT GORAKHPUR CITY’S AQI: A CRITICAL STUDY *Corresponding Author, 14.

[14] Lee, M., Lin, L., Chen, C. Y., Tsao, Y., Yao, T. H., Fei, M. H., & Fang, S. H. (2020). Forecasting Air Quality in Taiwan by Using Machine Learning. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-61151-7

[15] Liang, Y. C., Maimury, Y., Chen, A. H. L., & Juarez, J. R. C. (2020). Machine learning-based prediction of air quality. Applied Sciences (Switzerland), 10(24), 1–17. https://doi.org/10.3390/app10249151

[16] Liu, H., Li, Q., Yu, D., & Gu, Y. (2019). Air quality index and air pollutant concentration prediction based on machine learning algorithms. Applied Sciences (Switzerland), 9(19). https://doi.org/10.3390/app9194069

[17] Ma, J., Cheng, J. C. P., Lin, C., Tan, Y., & Zhang, J. (2019). Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques. Atmospheric Environment, 214. https://doi.org/10.1016/j.atmosenv.2019.116885

[18] Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., Tan, Y., Gan, V. J. L., & Wan, Z. (2020a). Identification of high impact factors of air quality on a national scale using big data and machine learning techniques. Journal of Cleaner Production, 244. https://doi.org/10.1016/j.jclepro.2019.118955

[19] Mahesh, T. R., Vinoth Kumar, V., Muthukumaran, V., Shashikala, H. K., Swapna, B., & Guluwadi, S. (2022). Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer. Journal of Sensors, 2022. https://doi.org/10.1155/2022/4649510

[20] Méndez, M., Merayo, M. G., & Núñez, M. (2023). Machine learning algorithms to forecast air quality: a survey. Artificial Intelligence Review, 56(9), 10031–10066. https://doi.org/10.1007/s10462-023-10424-4

[21] Mondal, S., Adhikary, A. S., Dutta, A., Bhardwaj, R., & Dey, S. (2024). Utilizing Machine Learning for air pollution prediction, comprehensive impact assessment, and effective solutions in Kolkata, India. Results in Earth Sciences, 2, 100030. https://doi.org/10.1016/j.rines.2024.100030

[22] Natarajan, S. K., Shanmurthy, P., Arockiam, D., Balusamy, B., & Selvarajan, S. (2024). Optimized machine learning model for air quality index prediction in major cities in India. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-54807-1

[23] Rahman, M. M., Nayeem, M. E. H., Ahmed, M. S., Tanha, K. A., Sakib, M. S. A., Uddin, K. M. M., & Babu, H. M. H. (2024). AirNet: predictive machine learning model for air quality forecasting using web interface. Environmental Systems Research, 13(1). https://doi.org/10.1186/s40068-024-00378-z

[24] Ravindiran, G., Rajamanickam, S., Kanagarathinam, K., Hayder, G., Janardhan, G., Arunkumar, P., Arunachalam, S., AlObaid, A. A., Warad, I., & Muniasamy, S. K. (2023). Impact of air pollutants on climate change and prediction of air quality index using machine learning models. Environmental Research, 239. https://doi.org/10.1016/j.envres.2023.117354

[25] Samad, A., Garuda, S., Vogt, U., & Yang, B. (2023). Air pollution prediction using machine learning techniques – An approach to replace existing monitoring stations with virtual monitoring stations. Atmospheric Environment, 310. https://doi.org/10.1016/j.atmosenv.2023.119987

[26] Sekeroglu, B., Ever, Y. K., Dimililer, K., & Al-Turjman, F. (2022). Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems. Data Intelligence, 4(3), 620–652. https://doi.org/10.1162/dint_a_00155

[27] Tien, P. W., Wei, S., Darkwa, J., Wood, C., & Calautit, J. K. (2022). Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review. In Energy and AI (Vol. 10). Elsevier B.V. https://doi.org/10.1016/j.egyai.2022.100198

[28] Wang, Y., Huang, L., Huang, C., Hu, J., & Wang, M. (2023). High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city. Environment International, 172. https://doi.org/10.1016/j.envint.2023.107752

[29] Wu, Q., & Lin, H. (2019). A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors. Science of the Total Environment, 683, 808–821. https://doi.org/10.1016/j.scitotenv.2019.05.288

[30] Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing, 2(1), 1–15. https://doi.org/10.3390/bdcc2010005

@article{1710528,
      author = {Salil Kumar Gupta, Praveen Kumar Yadav},
      title = {Predicting Air Quality Index (AQI) in Western Uttar Pradesh Cities Using Machine Learning Models: A Comparative Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {237-255},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710528.pdf},
      abstract = {The objective of this project is to use a variety of machine learning techniques to create a reliable predictive model for predicting the Air Quality Index (AQI) in Indian cities in Western Uttar Pradesh. The data came from the Central Pollution Control Board (CPCB) and covered the years January 2024 to December 2024. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) were the primary evaluation metrics used to compare the performance of the four machine learning models: Linear Regression, K-Nearest Neighbors (KNN), Decision Tree Regressor, and Random Forest Regressor. In terms of predicted accuracy, XGBoost outperformed the other algorithms among these models, exhibiting an impressive R2 value of 0.9967. Together with lower MSE and RMSE values, the XGBoost model demonstrated a significant decrease in MAE (3.5282) when compared to the other models, whose MAEs ranged from 18.148. These results suggested that the predictions were more accurate and had less volatility. Agra, Meerut, Ghaziabad, Bareilly, and Vrindavan were the five cities in which the study also examined AQI trends. According to the findings, the highest AQI recorded in Agra was 321.89 in November, while the highest AQI recorded in Meerut was 427.09 in the same month. The highest AQI in Bareilly was recorded in February (118.73), while the highest in Ghaziabad was recorded in January (413.57). October saw the highest AQI in Vrindavan (309.41). These cities' varying average AQIs were a reflection of seasonal variations in air pollution levels.},
      keywords = {Linear Regression, Random Forest, Decision Tree Regressor, K-Nearest Neighbors (KNN), Western Uttar Pradesh},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1710528-450}
  }