Artificial Intelligence Approach of Modeling of PM10 Emission close to a Steel Plant

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Kovacic:2013:QUALITY,
  author =       "Miha Kovacic and Sandra Sencic and Uros Zuperl",
  title =        "Artificial Intelligence Approach of Modeling of {PM10}
                 Emission close to a Steel Plant",
  booktitle =    "8th Research/Expert Conference with International
                 Participations, QUALITY 2013",
  year =         "2013",
  editor =       "Safet Brdarevic and Sabahudin Jasarevic",
  pages =        "305--310",
  address =      "Neum, Bosnia and Herzegovina",
  month =        "06-08 " # jun,
  organisation = "University of Zenica and Erlangen University and
                 Quality Association of Bosnia and Herzegovina",
  keywords =     "genetic algorithms, genetic programming, Steel plant,
                 PM10 concentrations, modeling, genetic programming",
  URL =          "http://www.quality.unze.ba/zbornici/QUALITY%202013/050-Q13-013.pdf",
  size =         "6 pages",
  abstract =     "To implement sound air quality policies, regulatory
                 agencies require tools to evaluate the outcomes and
                 costs associated with various emission reduction
                 strategies. The applicability of such tools can also
                 remain uncertain. It is furthermore known that
                 source-receptor models cannot be implemented through
                 deterministic modelling. The article presents an
                 attempt of PM10 emission modeling carried close to a
                 steel production area with the genetic programming
                 method. The daily PM10 concentrations, daily rolling
                 mill and steel plant production, meteorological data
                 (wind speed and direction, hourly average, air
                 temperature, hourly average and rainfall, daily
                 average), weekday and month number were used for
                 modelling during a monitoring campaign of almost half a
                 year (23.6.2010 to 12.12.2010). The genetic programming
                 modelling results show good agreement with measured
                 daily PM10 concentrations.",
  notes =        "http://quality.unze.ba/e/final.phps Kvalitet 2013
                 COBISS.SI-ID 2933499 jasarevic@mf.unze.ba;
                 sabahudinjasarevic@yahoo.com; sbrdarevic@mf.unze.ba
                 ASOCIJACIJA ZA KVALITET U BOSNI I HERCEGOVINI",
}

Genetic Programming entries for Miha Kovacic Sandra Sencic Uros Zuperl

Citations