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An evolutionary system for ozone concentration forecasting

Author

Listed:
  • Mauro Castelli

    (Universidade Nova de Lisboa)

  • Ivo Gonçalves

    (Universidade Nova de Lisboa
    University of Coimbra)

  • Leonardo Trujillo

    (Tree-Laboratory, Instituto Tecnológico de Tijuana)

  • Aleš Popovič

    (Universidade Nova de Lisboa
    University of Ljubljana, Faculty of Economics)

Abstract

Nowadays, with more than 50 % of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.

Suggested Citation

  • Mauro Castelli & Ivo Gonçalves & Leonardo Trujillo & Aleš Popovič, 0. "An evolutionary system for ozone concentration forecasting," Information Systems Frontiers, Springer, vol. 0, pages 1-10.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9706-2
    DOI: 10.1007/s10796-016-9706-2
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    References listed on IDEAS

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    1. Castelli, Mauro & Vanneschi, Leonardo & De Felice, Matteo, 2015. "Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case," Energy Economics, Elsevier, vol. 47(C), pages 37-41.
    2. Shancang Li & Li Da Xu & Shanshan Zhao, 2015. "The internet of things: a survey," Information Systems Frontiers, Springer, vol. 17(2), pages 243-259, April.
    3. Chittaranjan Hota & Shambhu Upadhyaya & Jamal Nazzal Al-Karaki, 2015. "Advances in secure knowledge management in the big data era," Information Systems Frontiers, Springer, vol. 17(5), pages 983-986, October.
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