IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i3d10.1007_s13198-020-01049-9.html
   My bibliography  Save this article

Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities

Author

Listed:
  • Alisha Banga

    (Indian Institute of Technology Roorkee Saharanpur Campus)

  • Ravinder Ahuja

    (Indian Institute of Technology Roorkee Saharanpur Campus)

  • Subhash Chander Sharma

    (Indian Institute of Technology Roorkee Saharanpur Campus)

Abstract

With an increase in the urban population, environmental pollution is drastically increased. Air pollution is one of the significant issues in smart cities. The higher value of PM2.5 can cause various health issues like respiratory disease, heart attack, lung disease, and fatigue. Predicting PM2.5 can help the administration to warn people at risk and make scientific measures to reduce pollution. Existing work has utilized various regression models to predict air pollution; however, different feature selection techniques with the regression algorithm have not yet been explored. This paper has implemented five feature selection techniques (namely, Recursive Feature Elimination, Analysis of Variance, Random Forest, Variance Threshold, and Light Gradient Boosting) to select the best features. Further, six regression algorithms and ensemble models (Extra Tree, Decision Tree, XGBoost, Random Forest, Light GBM, and AdaBoost) are applied to predict PM2.5 using python language on the dataset of five cities of China. The models are compared based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 parameters. We observed that the AdaBoost algorithm with the Light GBM feature selection technique gives the highest performance among all the five datasets. The highest performance values (MAE 0.07, RMSE 0.14, and R2 0.94) are given by the AdaBoost algorithm with LightGBM feature selection on the Chengdu dataset. The computed feature importance has shown that humidity, cbwd, dew point, and pressure play an essential role in air pollution.

Suggested Citation

  • Alisha Banga & Ravinder Ahuja & Subhash Chander Sharma, 2023. "Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 732-745, July.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:3:d:10.1007_s13198-020-01049-9
    DOI: 10.1007/s13198-020-01049-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-01049-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-020-01049-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiangshe Zhang & Weifu Ding, 2017. "Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong," IJERPH, MDPI, vol. 14(2), pages 1-19, January.
    2. Mauro Castelli & Fabiana Martins Clemente & Aleš Popovič & Sara Silva & Leonardo Vanneschi, 2020. "A Machine Learning Approach to Predict Air Quality in California," Complexity, Hindawi, vol. 2020, pages 1-23, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Md Shaik Amzad Basha & Peerzadah Mohammad Oveis, 2024. "Predictive modeling and benchmarking for diamond price estimation: integrating classification, regression, hyperparameter tuning and execution time analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(11), pages 5279-5313, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sasikumar Gurumoorthy & Aruna Kumari Kokku & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari, 2023. "Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    2. Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Yuxuan Ma, 2022. "An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.

    More about this item

    Keywords

    AQI; Regression models; PM2.5; Machine learning; Smart city;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:14:y:2023:i:3:d:10.1007_s13198-020-01049-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.