An evolutionary system for ozone concentration forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Mauro Castelli and Ivo Goncalves and 
                 Leonardo Trujillo and Ales Popovic",
  title =        "An evolutionary system for ozone concentration
  journal =      "Information Systems Frontiers",
  volume =       "19",
  number =       "5",
  pages =        "1123--1132",
  year =         "2017",
  month =        "1 " # oct,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Smart cities, Forecasting, Air quality",
  ISSN =         "1572-9419",
  DOI =          "doi:10.1007/s10796-016-9706-2",
  publisher =    "Springer",
  size =         "10 pages",
  abstract =     "Nowadays, with more than half 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.",
  notes =        "Also known as \cite{Castelli2017}",

Genetic Programming entries for Mauro Castelli Ivo Goncalves Leonardo Trujillo Ales Popovic