Comparative Analysis of Machine Learning Models Vis-a-Vis Regularization Models on Predictive Ability of Pollution Levels in Bangalore

Document Type : Original Research Paper

Authors

1 Department of Mechanical Engineering, BMS Institute of Technology & Management, Bengaluru – 54, Affiliated to Visvesvaraya Technological University Belagavi -18, Karnataka, India

2 AMC Engineering College, Bengaluru – 560083, Affiliated to Visvesvaraya Technological University Belagavi -18, Karnataka, India

10.22059/poll.2025.393694.2892

Abstract

The work brings in various dimensions to understand the importance of machine learning models in terms of predicting and understanding the variables which affect the concentration of Particulate matter PM2.5 and PM10 for Bangalore city. In this work Metrological variables, Pollutants are considered as inputs. In addition, the work highlights the differences achieved in terms of performance metric especially in terms of error variance and prediction power for particulate matter using Regularization, Bagging and Boosting techniques. It specifically brings about areas where these techniques can perform well and underperform. The work also compares how the models performed with and without features such as seasons. It was noticed the order of feature importance differed for regularization, boosting and bagging models. It was noticed that  Boosting techniques such as Xgboost had lower RMSE(9.1), MAPE (15.75) and higher R2 values (.72) for PM2.5 than other models however overfitting was noticed. However random forest had a lower R2 (.64) compared to boosting and RMSE (10.35) and MAPE (22.45) were slightly larger and tendency to overfit was lower.  To understand further, a new approach was created to diagnose where exactly these models perform well and underperform. The Absolute values were divided into percentile values and correlation was investigated with respect to error or residual values, adding to the uniqueness of this work. It was found that Extreme values tend to be correlated to larger residual values and those within the normal percentile range have lesser residual values.  

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Main Subjects

References

Banerjee, T., Singh, S. B., & Srivastava, R. K. (2011). Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar, India. Atmos. Res., 99(3–4), 505–517.
Barthwal, A., Acharya, D., & Lohani, D. (2023). Prediction and analysis of particulate matter (PM2.5 and PM10) concentrations using machine learning techniques. J. Ambient Intell. Humaniz. Comput., 14(3), 1323–1338.
Breiman, L. (2001). Random forests. Mach. Learn., 45, 5–32.
Bruno, F., Cocchi, D., & Trivisano, C. (2004). Forecasting daily high ozone concentrations by classification trees. Environmetrics, 15(2), 141–153.
Carslaw, D. C., & Taylor, P. J. (2009). Analysis of air pollution data at a mixed source location using boosted regression trees. Atmos. Environ., 43(22–23), 3563–3570.
Chang, Y. S., Chiao, H. T., Abimannan, S., Huang, Y. P., Tsai, Y. T., & Lin, K. M. (2020). An LSTM-based aggregated model for air pollution forecasting. Atmos. Pollut. Res., 11(8), 1451–1463.
Chen, J., de Hoogh, K., Gulliver, J., Hoffmann, B., Hertel, O., Ketzel, M., ... & Hoek, G. (2019). A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environ. Int., 130, 104934.
Chen, T., & Guestrin, C. (2016, August). XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (pp. 785–794).
Cortina–Januchs, M. G., Quintanilla–Dominguez, J., Vega–Corona, A., & Andina, D. (2015). Development of a model for forecasting of PM10 concentrations in Salamanca, Mexico. Atmos. Pollut. Res., 6(4), 626–634.
Department of Health and Human Services. (1997, July 18). Protection of human subjects; reports of the President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research. Fed. Regist., 62(138), 38477–38480. https://www.govinfo.gov/content/pkg/FR-1997-07-18/pdf/97-18577.pdf
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., & Hueglin, C. (2018). Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmos. Chem. Phys., 18(9), 6223–6239.
Gupta, P., Zhan, S., Mishra, V., Aekakkararungroj, A., Markert, A., Paibong, S., & Chishtie, F. (2021). Machine learning algorithm for estimating surface PM2.5 in Thailand. Aerosol Air Qual. Res., 21(11), 210105.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2013). Multivariate data analysis (8th ed.). Pearson Education Limited.
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in Python. Springer International.
Jia, M., Cheng, X., Zhao, T., Yin, C., Zhang, X., Wu, X., ... & Zhang, R. (2019). Regional air quality forecast using a machine learning method and the WRF model over the Yangtze River Delta, East China. Aerosol Air Qual. Res., 19(7), 1602–1613.
Kim, B. Y., Lim, Y. K., & Cha, J. W. (2022). Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms. Atmos. Pollut. Res., 13(10), 101547.
Kumar, U., & Jain, V. K. (2010). ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch. Environ. Res. Risk Assess., 24, 751–760.
Li, Y., Sha, Z., Tang, A., Goulding, K., & Liu, X. (2023). The application of machine learning to air pollution research: A bibliometric analysis. Ecotoxicol. Environ. Saf., 257, 114911.
Liaw, A. (2002). Classification and regression by randomForest. R News, 2(3).
Ma, J., Yu, Z., Qu, Y., Xu, J., & Cao, Y. (2020). Application of the XGBoost machine learning method in PM2.5 prediction: A case study of Shanghai. Aerosol Air Qual. Res., 20(1), 128–138.
Napi, N. N. L. M., Mohamed, M. S. N., Abdullah, S., Mansor, A. A., Ahmed, A. N., & Ismail, M. (2020, December). Multiple linear regression (MLR) and principal component regression (PCR) for ozone (O3) concentrations prediction. In IOP Conf. Ser.: Earth Environ. Sci. (Vol. 616, No. 1, p. 012004). IOP Publishing.
Pan, B. (2018, February). Application of XGBoost algorithm in hourly PM2.5 concentration prediction. In IOP Conf. Ser.: Earth Environ. Sci. (Vol. 113, p. 012127). IOP Publishing.
Reyes, H. J., Vaquera, H., & Villaseñor, J. A. (2010). Estimation of trends in high urban ozone levels using the quantiles of GEV. Environmetrics, 21(5), 470–481.
Russo, A., Lind, P. G., Raischel, F., Trigo, R., & Mendes, M. (2015). Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos. Pollut. Res., 6(3), 540–549.Saravani, M. J., Noori, R., Jun, C., Kim, D., Bateni, S. M., Kianmehr, P., & Woolway, R. I. (2025). Predicting Chlorophyll-a concentrations in the world’s largest lakes using Kolmogorov–Arnold networks. Environ. Sci. Technol., 59(3), 1801–1810.
Saravani, M. J., Saadatpour, M., & Shahvaran, A. R. (2024). A web GIS based integrated water resources assessment tool for Javeh Reservoir. Expert Syst. Appl., 252, 124198.
Sharma, P., Chandra, A., & Kaushik, S. C. (2009). Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City. Environ. Monit. Assess., 157, 105–112.
Sihag, P., Kumar, V., Afghan, F. R., Pandhiani, S. M., & Keshavarzi, A. (2019). Predictive modeling of PM2.5 using soft computing techniques: Case study—Faridabad, Haryana, India. Air Qual. Atmos. Health, 12(12), 1511–1520.
Suleiman, A., Tight, M. R., & Quinn, A. D. (2016). Hybrid neural networks and boosted regression tree models for predicting roadside particulate matter. Environ. Model. Assess., 21, 731–750.
Venkataraman, V., Prasad, S., Aswathanarayana, B., Barigidad, S., & Nayak, V. (2020). Development of time series models for various pollutants in Bangalore city using the Akaike information criterion. Eng. Appl. Sci. Res., 47(3), 249–263.
Wang, S., Ren, Y., & Xia, B. (2023). PM2.5 and O3 concentration estimation based on interpretable machine learning. Atmos. Pollut. Res., 14(9), 101866.
Pollution
Volume 11, Issue 4
October 2025
Pages 1419-1432

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