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Artificial neural network model for predicting air pollution. Case study of the Moravica district, Serbia

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An example of artificial neural network model for predicting air pollution has been presented. The research was conducted in Serbia, the Moravica District, on the territory of two municipalities (LuCani and Ivanjica) and the town Čačak. The level of air pollution was classified by a neural network model according to the input data: municipality, site, year, levels of soot, sulfur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter. The model was evaluated using a lift chart and a root mean square error (RMSE) has been determined, whose value was 0.0635. A multilayer perceptron has also been created and trained with a back propagation algorithm. The neural network was tested with the data mining extensions (DMX) queries. The results have been obtained for air pollution based on new input data that can be used to predict the level of pollution in future if new measurements are carried out. A web-based application was designed for displaying the results.
Rocznik
Strony
129--139
Opis fizyczny
Bibliogr. 24 poz., tab., rys.
Twórcy
  • Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, Čačak; 32000, Serbia
autor
  • Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, Čačak; 32000, Serbia
autor
  • Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, Čačak; 32000, Serbia
  • Faculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, Čačak; 32000, Serbia
Bibliografia
  • [1] PATRONAS D., KARIDA A., PAPADOPOULOU A., PISIHA A., XIPOLITOS K., KOKKINIS G., VOSNAIKOS K., GRAMMATIKIS B., VOSNIAKOS F., VASDEKIS K., Air pollution and noise pollution due to traffic in three Greek cities, J. Environ. Prot. Ecol., 2009, 10 (2), 332.
  • [2] HAIDUC C., ROBA L., BOBOS L., FECHETE-RADU L., Urban aerosols pollution. Case study: Cluj– Napoca City, J. Environ. Prot. Ecol., 2009, 10 (3), 611.
  • [3] HE G., FAN M., ZHOU M., The effect of air pollution on mortality in China: Evidence from the 2008 Beijing Olympic Games, J. Environ. Econ. Manage., 2016, 79, 18.
  • [4] XIAO S., LIU R., WEI Y., FENG L., LVA X., TANG F., Air pollution and blood lipid markers level. Estimating short- and long-term effects on elderly hypertension inpatients complicated with or without type 2 diabetes, Environ. Pollut., 2016, 215, 135.
  • [5] POPESCU F., IONEL I., LONTIS N., CALIN L., DUNGAN I.L., Air quality monitoring in an urban agglomeration, Rom. J. Phys., 2011, 56, 495.
  • [6] ADAMS M., YIANNAKOULIAS N., KANAROGLOU P., Air pollution exposure: An activity pattern approach for active transportation, Atmos. Environ., 2016, 140, 52.
  • [7] MCCREDDIN A., ALAM M.S., MCNABOLA A., Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling, Monte Carlo simulation and artificial neural network approaches, Int. J. Hyg. Environ. Health, 2015, 218 (1), 107.
  • [8] VAKILI M., SABBAGH-YAZDI S., KALHOR K., KHOSROJERDI S., Using Artificial neural networks for prediction of global solar radiation in tehran considering particulate matter air pollution, Energy Proc., 2015, 74, 1205.
  • [9] CHAN K., JIAN L., Identification of significant factors for air pollution levels using a neural network based knowledge discovery system, Neurocomp., 2013, 99, 564.
  • [10] 2011 Census of Population, Households and Dwellings in The Republic of Serbia, Comparative overview of the number of population in 1948, 1953, 1691, 1971, 1981, 1991, 2002 and 2011, Statistical Office of the Republic of Serbia, Belgrade, 2014.
  • [11] The neoliberal paradigm and winds from Olympus http://www.nspm.rs/images/stories/01maja /aa2/Srbija-u-Evropi.jpg, retrieved on 26/9/2016
  • [12] Moravica district, https://sr.wikipedia.org/wiki/Моравички_управни_округ#/media/File:Moraica_in_Serbia.svg, retrieved 26/9/2016.
  • [13] ISO 9835:1993, Ambient air – Determination of a black smoke index.
  • [14] SRPS ISO 6767:1997. Ambient air – Determination of the mass concentration of sulfur dioxide –Tetrachloromercurate (TCM)/pararosaniline method.
  • [15] Nitric oxide and nitrogen dioxide. Method 6014, Issue 1, 4th Ed., NIOSH Manual of AnalyticalMethods (NMAM), 1994.
  • [16] RAMZIN S., Particulate matter (air). Determination of soluble, insoluble matter and ash VMK 043. Manual for communal hygiene, Medicinska Knjiga, Beograd 1966.
  • [17] Statistics of the Institute of Public Health in Čačak, Serbia, http://www.zdravljecacak.org/stranice /Statisticki%20god.php, retrieved 19/7/2016.
  • [18] Book of regulations on conditions and requirements for monitoring of air quality, Official Gazette of the Republic of Serbia, No. 11/2010.
  • [19] Clean Air Copenhagen. Air Quality Challenges and Solutions, The Danish Ecological Council, 2014.
  • [20] Law on Air Protection in Serbia, Official Gazette of the Republic of Serbia, Nos. 36/2009 and 10/2013
  • [21] ISO/IEC 2382-34:1999 Information technology – Vocabulary – Part 34: Artificial intelligence – Neural Networks, 1999.
  • [22] DRAPER C., REICHLE R., JEU R., NAEMI V., PARINUSSA R., WAGNER W., Estimating root mean square errors in remotely sensed soil moisture over continental scale domains, Remote Sens. Environ., 2013, 137, 288.
  • [23] Data mining extensions queries, https://msdn.microsoft.com/en-us/library/ms174788.aspx, retrieved 26/7/2016
  • [24] BLAGOJEVIC M., MICIĆ Ž., Web-based intelligent report e-learning system using data mining techniques, Comp. Electr. Eng. 2013, 39 (2), 465.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-53248c26-4741-48e2-afc3-48e43e0e76df
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