ICTACT Journal on Soft Computing

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Prediction of AQI Using Hybrid Approach in Machine Learning


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1 Department of Computer Science and Engineering, Baba Mastnath University, India., India
     

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Forecast of urban air pollutant concentrations must deal with a growth in the volume of environmental monitoring data as well as complex changes in the air pollutants. This initiates the need of efficient prediction techniques to increase prediction accuracy and stop pollution causing issues. In this paper, the combination of Random Forest Regression (RFR) and Support Vector Regression (SVR) based machine learning model is proposed to predict the AQI values. Daily meteorological and air pollution data between March 2020 and June 2022 from Jind city in Haryana were used for model training and test the model’s results. First, the important factors affecting air quality are extracted and null values are replaced by their mean values to handle the irregularities in the dataset. Second, the Random Forest regression machine is used for model training and prediction of the value, the SVR model is used in correction of residual items, and finally the predicted values of AQI are obtained. The experimental results showed that the proposed prediction model of RFR-SVR had a better prediction result than the standard Random Forest Regression, support vector regression machine learning. RMSE, R2, MAPE are used as evaluation indicators to evaluate the performance of the proposed model.

Keywords

Air Quality Index, RFR, Support Vector Regression, Hybrid Model, Coefficient Metric.
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  • R.K. Grace and S. Manju, “A Comprehensive Review of Wireless Sensor Networks Based Air-Pollution Monitoring Systems”, Wireless Personal Communications, Vol. 87, pp. 2499-2515, 2019.

  • R. Januszek, B. Staszczak, Z. Siudak, J. Bartus, K. Plens, S. Bartus and D. Dudek, “The Relationship between increased Air Pollution expressed as PM10 Consentration and the Frequency of Percutaneous Coronary Interventions in Patients with Accute Coronary Syndromes - A Seasonal Differences”, Environmental Science and Pollution Research, Vol. 27, pp. 21320-21330, 2020.

  • L. Gharibvand, D. Shavlik, M. Ghamsary, W.L. Beeson, S. Soret, R. Knutsen and S.F. Knutsen, “The Association between Ambient Fine Particulate Air Pollution and Lung Cancer Incidence: Results from the AHSMOG-2 Study”, Environmental Health Perspect, Vol. 125, No. 3, pp. 378- 384, 2017.

  • R. Janarthanan, P. Partheeban, K. Somasundaram and P.N. Elamparithi, “A Deep Learning Approach for Prediction of Air Quality Index in a Metropolitan City”, Sustainable Cities and Society, Vol. 67, pp. 1-11, 2021.

  • L. Spinelle, M. Gerboles, M.G. Villani, M. Aleixandre and F. Bonavitacola, “Field Calibration of a Cluster of Low Cost Commercially available Sensors for Air Quality Monitoring. Part B: NO, CO and CO2”, Sensors and Actuators, Vol. 238, pp. 706-715, 2017.

  • Q. Wu and H. Lin, “A Novel Optimal- Hybrid Model for Daily Air Quality Index Prediction considering Air Pollutant Factors”, Science of the Total Environment, Vol. 683, pp. 808-821, 2019.

  • M. Mittal, L.M. Goyal, J.K. Sethi and D.J. Hemanth, “Monitoring the Impact of Economic Crisis on Crime in India using Machine Learning”, Computational Economics, Vol. 53, pp. 1467-1485, 2019.

  • J.K. Sethi and M. Mittal, “Ambient Air Quality Estimation Using Supervised Learning Techniques”, EAI Endorsed Transactions, Vol. 6, No. 22, pp. 1-10, 2019.

  • H. Liu, Q. Li, D. Yu and Y. Gu, “Air Quality Index and Air Pollutant Concentration Prediction based on Machine Learning Algorithms”, MDPI, Vol. 4069, pp. 1-9, 2019.

  • S. Bhattacharya and S. Shahnawaz, “Using Machine Learning to Predict Air Quality Index in New Delhi”, Proceedings of International Conference on Machine Learning, pp. 1-7, 2021.

  • M. Castelli, F.M. Clemente, A. Popovik, S. Silva and L. Vanneschi, “A Machine Learning Approach to Predict Air Quality in California”, Complexity, Vol. 2020, pp. 1-23, 2020.

  • V.A. Bhat, A.S. Manek and P. Mishra, “Machine Learning based Prediction System for Detecting Air Pollution”, International Journal of Engineering Research and Technology, Vol. 8, No. 9, pp. 155-159, 2019.

  • G. Corani and M. Scanagatta, “Air Pollution Prediction via Multi Label Classification”, Environmental Modelling and Software, Vol. 80, No. 7, pp. 259-264, 2016.

  • J. Fan, L. Wu, F. Zhang, H. Cai, X. Wang, X. Lu and Y. Xiang, “Evaluation the Effect of Air Pollution on Global and Diffuse Solar Rediation Prediction using Spot Vector Machine Modeling based on Sunshine Duration and Air temperature”, Renewable and Sustainable Energy Reviews, Vol. 94, pp. 732-747, 2018.

  • D. Qin, J. Yu, G. Zou, R. Yong, Q. Zhao and B. Zhang, “A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration”, IEEE Access, Vol. 7, pp. 20050-20059, 2019.

  • B. Liu, Y. Jin and C. Li, “Analysis and Prediction of Air Quality in Nanjing from Autumn 2018 to Summer 2019 using PCR-SVR-ARMA combined Model”, Scientific Reports, Vol. 348, pp. 1-14, 2021.

  • J.K. Sethi and M. Mittal, “Prediction of Air Quality Index using Hybrid Machine Learning Algorithm”, Proceedings of International Conference on Machine Learning, pp. 1-13, 2019.

  • C. Wen, S. Liu, X. Yao, L. Peng, X. Li, Y. Hu and T. Chi, “A Novel Spatiotemporal Convolutional Long Short-Term Neural Network for Air Pollution Prediction”, Science of the Total Environment, Vol. 654, pp. 1091-1099, 2019.

  • J.K. Sethi and M. Mittal, “Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: using Machine Learning Techniques”, Disaster Medicine and Public Health Preparedness, Vol. 16, No. 2, pp. 604-611, 2022.


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  • Prediction of AQI Using Hybrid Approach in Machine Learning

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Authors

Reema Gupta
Department of Computer Science and Engineering, Baba Mastnath University, India., India
Priti Singla
Department of Computer Science and Engineering, Baba Mastnath University, India., India

Abstract


Forecast of urban air pollutant concentrations must deal with a growth in the volume of environmental monitoring data as well as complex changes in the air pollutants. This initiates the need of efficient prediction techniques to increase prediction accuracy and stop pollution causing issues. In this paper, the combination of Random Forest Regression (RFR) and Support Vector Regression (SVR) based machine learning model is proposed to predict the AQI values. Daily meteorological and air pollution data between March 2020 and June 2022 from Jind city in Haryana were used for model training and test the model’s results. First, the important factors affecting air quality are extracted and null values are replaced by their mean values to handle the irregularities in the dataset. Second, the Random Forest regression machine is used for model training and prediction of the value, the SVR model is used in correction of residual items, and finally the predicted values of AQI are obtained. The experimental results showed that the proposed prediction model of RFR-SVR had a better prediction result than the standard Random Forest Regression, support vector regression machine learning. RMSE, R2, MAPE are used as evaluation indicators to evaluate the performance of the proposed model.

Keywords


Air Quality Index, RFR, Support Vector Regression, Hybrid Model, Coefficient Metric.

References