IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v114y2022i2d10.1007_s11069-022-05463-z.html
   My bibliography  Save this article

Spatial Air Quality Index and Air Pollutant Concentration prediction using Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF): a case study in India

Author

Listed:
  • Shwet Ketu

    (University Of Petroleum And Energy Studies(UPES))

Abstract

In the last decade, air pollution has become one of the vital environmental issues and has expanded its wings day by day. Prediction of air quality plays a crucial role in warning people about the air quality levels. With the help of this, we can make the proper mechanism for reducing the overall impact of bad air quality on individuals’ health. In this paper, we are focused on developing a mechanistic and quantitative prediction model for the prediction of the Air Quality Index (AQI) and Air Pollutant Concentration (NOx) levels with a clear environmental interpretation. The proposed model is based on the Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF). For the experimental analysis, the seven well-established machine learning models have been taken, and these models are compared with our proposed model to find out their suitability and correctness. The Mean Absolute Percentage Error (MAPE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Coefficient of Determination (R2 score) have been used to validate the performance of prediction models. For the prediction of AQI and NOx, the data of the Central Pollution Control Board of India has been taken. The proposed model performs superior as compared to other prediction models with better accuracy and a higher prediction rate. This work also explains that linking machine learning with sensor-generated AQI data for air quality prediction is an adequate and appropriate way to solve some related environment glitches. Apart from this, the impact of air pollution on individuals’ health due to high levels of AQI, NOx, and other pollutants with the possible solutions has also been covered.

Suggested Citation

  • Shwet Ketu, 2022. "Spatial Air Quality Index and Air Pollutant Concentration prediction using Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF): a case study in India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 2109-2138, November.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:2:d:10.1007_s11069-022-05463-z
    DOI: 10.1007/s11069-022-05463-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05463-z
    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/s11069-022-05463-z?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. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Cleland, John G. & van Ginneken, Jerome K., 1988. "Maternal education and child survival in developing countries: The search for pathways of influence," Social Science & Medicine, Elsevier, vol. 27(12), pages 1357-1368, January.
    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. Angel Hsu & Xuewei Wang & Jonas Tan & Wayne Toh & Nihit Goyal, 2022. "Predicting European cities’ climate mitigation performance using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

    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. Susanna M Makela & Rakhi Dandona & T R Dilip & Lalit Dandona, 2013. "Social Sector Expenditure and Child Mortality in India: A State-Level Analysis from 1997 to 2009," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-10, February.
    2. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    3. Jayanta Kumar Bora & Rajesh Raushan & Wolfgang Lutz, 2019. "The persistent influence of caste on under-five mortality: Factors that explain the caste-based gap in high focus Indian states," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    4. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    5. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    6. Jeroen Klomp & Jakob De Haan, 2008. "Effects of Governance on Health: a Cross‐National Analysis of 101 Countries," Kyklos, Wiley Blackwell, vol. 61(4), pages 599-614, November.
    7. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    8. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    9. Sánchez, Gustavo Crespo & Monteagudo Yanes, José Pedro & Pérez, Milagros Montesino & Cabrera Sánchez, Jorge Luis & Padrón, Arturo Padrón & Haeseldonckx, Dries, 2020. "Efficiency in electromechanical drive motors and energy performance indicators for implementing a management system in balanced animal feed manufacturing," Energy, Elsevier, vol. 194(C).
    10. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    11. Asif Afzal & Saad Alshahrani & Abdulrahman Alrobaian & Abdulrajak Buradi & Sher Afghan Khan, 2021. "Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms," Energies, MDPI, vol. 14(21), pages 1-22, November.
    12. Antonio Attanasio & Marco Savino Piscitelli & Silvia Chiusano & Alfonso Capozzoli & Tania Cerquitelli, 2019. "Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates," Energies, MDPI, vol. 12(7), pages 1-25, April.
    13. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    14. Frost, Michelle Bellessa & Forste, Renata & Haas, David W., 2005. "Maternal education and child nutritional status in Bolivia: finding the links," Social Science & Medicine, Elsevier, vol. 60(2), pages 395-407, January.
    15. Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load," Energies, MDPI, vol. 15(14), pages 1-20, July.
    16. Hannum, Emily & Buchmann, Claudia, 2005. "Global Educational Expansion and Socio-Economic Development: An Assessment of Findings from the Social Sciences," World Development, Elsevier, vol. 33(3), pages 333-354, March.
    17. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    18. Heaton, Tim B. & Forste, Renata & Hoffmann, John P. & Flake, Dallan, 2005. "Cross-national variation in family influences on child health," Social Science & Medicine, Elsevier, vol. 60(1), pages 97-108, January.
    19. Chandan Kumar & Prashant Kumar Singh & Rajesh Kumar Rai, 2012. "Under-Five Mortality in High Focus States in India: A District Level Geospatial Analysis," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-15, May.
    20. Muhammad Adeel Abbasa & Zeshan Iqbal, 2022. "Double Auction used Artificial Neural Network in Cloud Computing," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 65-76, June.

    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:nathaz:v:114:y:2022:i:2:d:10.1007_s11069-022-05463-z. 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.