IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v23y2021i4d10.1007_s10668-020-00841-8.html
   My bibliography  Save this article

Assessment and prediction of surface ozone in Northwest Indo-Gangetic Plains using ensemble approach

Author

Listed:
  • Madhvi Rana

    (Thapar University)

  • Susheel K. Mittal

    (Thapar University)

  • Gufran Beig

    (Indian Institute of Tropical Meteorology)

Abstract

The earth’s surface ozone levels are becoming very significant due to their negative impact on human health, vegetation and climate. In this study, the methodology based on ensemble approach embodied linear and nonlinear behaviors was developed. It was applied for prediction of ozone concentration using dataset (2013–2016) of gaseous pollutants (O3, CO, NOx, MHC, TNMHCs) and meteorological variables as input variables. The daily O3 max/O3 min ratio of 10.9 marks the peculiar ozone pollution in the area. The fourteen prediction algorithms and their possible combinations of ensemble models were employed in this paper. Compared with individual models, the ensemble model approach showed an index of agreement of 0.91, the accuracy of 95.5% and mean absolute error of − 0.001 ppb between the predicted and observed diurnal cycle and daily averaged data of the year 2016 for benchmark analysis.

Suggested Citation

  • Madhvi Rana & Susheel K. Mittal & Gufran Beig, 2021. "Assessment and prediction of surface ozone in Northwest Indo-Gangetic Plains using ensemble approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5715-5738, April.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:4:d:10.1007_s10668-020-00841-8
    DOI: 10.1007/s10668-020-00841-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-020-00841-8
    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/s10668-020-00841-8?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. Hassanzadeh, S. & Hosseinibalam, F. & Omidvari, M., 2008. "Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(10), pages 2317-2327.
    2. O. R. Cooper & D. D. Parrish & A. Stohl & M. Trainer & P. Nédélec & V. Thouret & J. P. Cammas & S. J. Oltmans & B. J. Johnson & D. Tarasick & T. Leblanc & I. S. McDermid & D. Jaffe & R. Gao & J. Stith, 2010. "Increasing springtime ozone mixing ratios in the free troposphere over western North America," Nature, Nature, vol. 463(7279), pages 344-348, January.
    3. Durai Sundaramoorthi, 2014. "A data-integrated simulation model to forecast ground-level ozone concentration," Annals of Operations Research, Springer, vol. 216(1), pages 53-69, May.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    Full references (including those not matched with items on IDEAS)

    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. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    3. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    4. Jakub Nowotarski, 2013. "Short-term forecasting of electricity spot prices using model averaging (Krótkoterminowe prognozowanie spotowych cen energii elektrycznej z wykorzystaniem uśredniania modeli)," HSC Research Reports HSC/13/17, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    6. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    7. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    8. Mauro Costantini & Ulrich Gunter & Robert M. Kunst, 2017. "Forecast Combinations in a DSGE‐VAR Lab," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(3), pages 305-324, April.
    9. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    10. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
    11. Pär Österholm, 2009. "Incorporating Judgement in Fan Charts," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(2), pages 387-415, June.
    12. Peng W. He & Andrew Grant & Joel Fabre, 2013. "Economic value of analyst recommendations in Australia: an application of the Black–Litterman asset allocation model," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(2), pages 441-470, June.
    13. Bell, William Paul, 2008. "Adaptive Interactive Profit Expectations and Small World Networks," MPRA Paper 37924, University Library of Munich, Germany.
    14. repec:cup:judgdm:v:8:y:2013:i:2:p:91-105 is not listed on IDEAS
    15. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    16. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    17. Elkin Castaño & Luis Fernando Melo, 1998. "Métodos de Combinación de Pronósticos: Una Aplicación a la Inflación Colombiana," Borradores de Economia 109, Banco de la Republica de Colombia.
    18. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    19. Joseph Kadane & Javier Girón & Daniel Peña & Peter Fishburn & Simon French & D. Lindley & Giovanni Parmigiani & Robert Winkler, 1993. "Several Bayesians: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(1), pages 1-32, December.
    20. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    21. Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.

    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:endesu:v:23:y:2021:i:4:d:10.1007_s10668-020-00841-8. 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.