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A data-integrated simulation model to forecast ground-level ozone concentration

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  • Durai Sundaramoorthi

Abstract

Elevated ground-level ozone is hazardous to people’s health and destructive to the environment. This research develops a novel data-integrated simulation to forecast ground-level ozone (SIMGO) concentration based on a real data set collected from seven monitoring sites in the Dallas-Fort Worth area between January 1, 2005 and December 31, 2007. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for building the simulation. Classification and Regression Trees (CART), data mining tools for prediction and classification, were used to develop two tree structures in order to forecast ground-level ozone based on factors such as solar radiation and outdoor temperature. Kernel density estimation is used to estimate continuous distributions for the ground-level ozone concentration for seven days in advance. One week forecasts obtained from SIMGO for different months of a year is presented. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:53-69:10.1007/s10479-012-1163-9
    DOI: 10.1007/s10479-012-1163-9
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    References listed on IDEAS

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    1. Durai Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2009. "A data-integrated simulation model to evaluate nurse–patient assignments," Health Care Management Science, Springer, vol. 12(3), pages 252-268, September.
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    Cited by:

    1. 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.

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