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Comparison of static MLP and dynamic NARX neural networks for forecasting of atmospheric PM10 and SO2 concentrations in an industrial site of Turkey

References

  • Akyürek, Ö., Arslan, O., and Karademir, A. 2013. SO2 ve PM10 Hava Kirliliği Parametrelerinin CBS ile Konumsal Analizi: Kocaeli Örneği. 4. Coğrafi Bilgi Sistemleri Kongresi, Ankara, Türkiye, 1–12.
  • AlKasassbeh, M., Sheta, A. F., Faris, H., and Turabieh, H. 2013. Prediction of PM10 and TSP air pollution parameters using artificial neural network autoregressive, external input models: A case study in Salt. Jordan. Middle-East Journal of Scientific Research 14(7):999–1009.
  • Asadollahfardi, G., Madinejad, M., Aria, S. H., and Motamadi, V. 2016. Predicting particulate matter (PM2.5) concentrations in the air of Shahr-e Ray City, Iran, by using an artificial neural network. Environmental Quality Management 25(4):71–83.
  • Asadollahfardi, G., Zangooei, H., and Aria, S. H. 2016. Predicting PM2.5 concentrations using artificial neural networks and Markov chain, a case study Karaj City. Asian Journal of Atmospheric Environment 10(2):67–79.
  • Ayturan, Y. A., Öztürk, A., and Ayturan, Z. C. 2017. Modelling of PM10 pollution in Karatay District of Konya with artificial neural network. Journal of International Environmental Application & Science 12(3):256–263.
  • Biancofiore, F., Busilacchio, M., Verdecchia, M., et al. 2017. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research 8(4):652–659.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., and Cadenas, R. 2016. Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México. Neural Computing and Applications 27(8):2417–2428.
  • Cadenas, E., Rivera, W., Campos-Amezcua, R., and Heard, C. 2016. Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies 9(2):109.
  • Caselli, M., Trizio, L., Gennaro, D. G., and Ielpo, P. 2009. A simple feedforward neural network for the PM10 forecasting: Comparison with a radial basis function network and a multivariate linear regression model. Water, Air, and Soil Pollution 201(1):365–377.
  • Çevre ve Şehircilik Bakanlığı | Ulusal Hava Kalite İzleme Ağı. 2019, September 1. https://www.havaizleme.gov.tr/.
  • Ceylan, Z., and Bulkan, S. 2018. Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City. Global Nest Journal 20(2):281–290.
  • Deng, X., Zhang, F., Rui, W., et al. 2013. PM2.5-induced oxidative stress triggers autophagy in human lung epithelial A549 cells. Toxicology in Vitro 27(6):1762–1770.
  • Diaconescu, E. 2008. The use of NARX neural networks to predict chaotic time series. Wseas Transactions on Computer Research 3(3):182–191.
  • Dotse, S. Q., Petra, M. I., Dagar, L., and De Silva, L. C. 2018. Application of computational intelligence techniques to forecast daily PM10 exceedances in Brunei Darussalam. Atmospheric Pollution Research 9(2):358–368.
  • Ele, S. I., and Adesola, W. A. 2015. Artificial neuron network implementation of Boolean logic gates by perceptron and threshold element as neuron output function. International Journal of Science and Research (IJSR) 4(9):637–641.
  • Fernandez-Ferrero, A. F., Sáenz, J., Berastegi, G. I., and Fernández, J. 2009. Evaluation of statistical downscaling in short range precipitation forecasting. Atmospheric Research 94(3):448–461.
  • Gardner, M. W., and Dorling, S. R. 1998. Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment 32(14–15):2627–2636.
  • Ghorbani, M. A., Khatibi, R., Karimi, V., Yaseen, Z. M., and Zounemat-Kermani, M. 2018. Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows. Water Resources Management 32(13):4201–4215.
  • Goss, C. H., Newsom, S. A., Schildcrout, J. S., Sheppard, L., and Kaufman, J. D. 2004. Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis. American Journal of Respiratory and Critical Care Medicine 169(7):816–821.
  • Goudarzi, G., Sahar Geravandi, S., Idani, E., et al. 2016. An evaluation of hospital admission respiratory disease attributed to sulfur dioxide ambient concentration in Ahvaz from 2011 through 2013. Environmental Science and Pollution Research International 23(21):22001–22007.
  • Haykin, S. 1998. Neural Networks: A Comprehensive Foundation. 2nd ed. New Delhi, India: Pearson Prentice Hall.
  • Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z. 2018. Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water 10(1543):1–16.
  • Jaikumar, R., Nagendra, S. M., and Sivanandan, R. 2018. Development of NARX based neural network model for predicting air quality near busy urban corridors. In Recent Developments and the New Direction in Soft-Computing Foundations and Applications, Studies in Fuzziness and Soft Computing book series. Cham, Switzerland: Springer International Publishing, 581–593.
  • Kaplan, Y., Saray, U., and Azkeskin, E. 2014. Hava Kirliliğine Neden olan PM10 ve SO2 maddesinin Yapay Sinir Ağı kullanılarak Tahmininin Yapılması ve Hata Oranının Hesaplanması. Afyon Kocatepe University Journal of Sciences and Engineering 14:1–6.
  • Karri, R. R., and Sahu, J. N. 2018. Process optimization and adsorption modeling using activated carbon derived from palm oil kernel shell for Zn (II) disposal from the aqueous environment using differential evolution embedded neural network. Journal of Molecular Liquids 265:592–602.
  • Koschwitz, D., Frisch, J., and van Treeck, C. 2018. Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale. Energy 165:134–142.
  • Krause, P., Boyle, D. P., and Bäse, F. 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5:89–97.
  • Kumar, G. S., and Rajasekhar, K. 2017. Performance analysis of Levenberg-Marquardt and Steepest Descent algorithms based ANN to predict compressive strength of SIFCON using manufactured sand. Engineering Science and Technology, an International Journal 20:1396–1405.
  • Kumar, A., and Sharma, M. P. 2016a. Assessment of risk of GHG emissions from Tehri hydropower reservoir, India. Human and Ecological Risk Assessment: An International Journal 22(1):71–85.
  • Kumar, A., and Sharma, M. P. 2016b. A modeling approach to assess the greenhouse gas risk in Koteshwar hydropower reservoir, India. Human and Ecological Risk Assessment: An International Journal 22(8):1651–1664.
  • Kumar, A., Yang, T., and Sharma, M. P. 2019a. Greenhouse gas measurement from Chinese freshwater bodies: A review. Journal of Cleaner Production 233:368–378.
  • Kumar, A., Yang, T., and Sharma, M. P. 2019b. Long-term prediction of greenhouse gas risk to the Chinese hydropower reservoirs. Science of the Total Environment 646:300–308.
  • Lin, T., and Horne, B. G. 1996. Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks 7(6):1329–1338.
  • Lv, J., Guo, C., Shen, Z. P., Zhao, M., and Zhang, Y. 2007. Summary of Artificial Neuron Model Research. 33rd Annual Conference of the IEEE Industrial Electronics Society, 677–682.
  • Manzato, A. 2007. Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts. Atmospheric Research 83(2–4):349–363.
  • Mekparyup, J., and Saithanu, K. 2013. Development of neural network technique for prediction of PM10 concentration in the industrial area, at the East of Thailand. Applied Mathematical Sciences 7(93):4631–4638.
  • Mena, R., Rodríguez, F., Castilla, M., and Arahal, M. R. 2014. A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy and Buildings 82:142–155.
  • Moazami, S., Noori, R., Amiri, B. J., Yeganeh, B., Partani, S., and Safavi, S. 2016. Reliable prediction of carbon monoxide using developed support vector machine. Atmospheric Pollution Research 7(3):412–418.
  • Özbay, B. 2012. Modeling the effects of meteorological factors on SO2 and PM10 concentrations with statistical approaches. Clean - Soil, Air, Water 40(6):571–577.
  • Pires, J. C., Alvim–Ferraz, M. C., Pereira, M. C., and Martins, F. G. 2010. Prediction of PM10 concentrations through multi–gene genetic programming. Atmospheric Pollution Research 1(4):305–310.
  • Powell, K. M., Sriprasad, A., Cole, W. J., and Edgar, T. F. 2014. Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy 74:877–885.
  • Press, W. H., Teukolsky, S. A., and Vetterling, W. T. 1988. Numerical Recipes in C. Cambridge, UK: Cambridge University Press.
  • Riverol, C., Hosein, N., and Singh, A. 2019. Forecasting reliability using non-linear autoregressive external input (NARX) neural network. Life Cycle Reliability and Safety Engineering 8:165–174.
  • Şahin, Ü. A., Bayat, C., and Uçan, O. N. 2011. Application of cellular neural network (CNN) to the prediction of missing air pollutant data. Atmospheric Research 101:314–326.
  • Sainlez, M., and Heyen, G. 2013. Comparison of supervised learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill. Journal of Computational and Applied Mathematics 246(13):329–334.
  • Schornobay-Lui, E., Hanisch, W. S., Corrêa, E. M., and Corrêa, N. A. 2019. Prediction of short and medium term PM10 concentration using artificial neural networks. Management of Environmental Quality: An International Journal 30(2):414–436.
  • Slaughter, J. C., Lumley, T., Sheppard, L., Koenig, J. Q., and Shapiro, G. G. 2003. Effects of ambient air pollution on symptom severity and medication use in children with asthma. Annals of Allergy Asthma and Immunology 91(4):346–353.
  • Sofuoglu, S. C., Sofuoglu, A., Birgili, S., and Tayfur, G. 2006. Forecasting ambient air SO2 concentrations using artificial neural networks. Energy Sources, Part B: Economics, Planning and Policy 1(2):127–136.
  • Tecer, L. H. 2007. Prediction of SO2 and PM concentrations in a coastal mining area (Zonguldak, Turkey) using an artificial neural network. Polish Journal of Environmental Studies 16(4):633–638.
  • Utkan, Ö., and Taner, S. 2014. Impacts of meteorological factors on PM10: Artificial neural networks (ANN) and multiple linear regression (MLR) approaches. Environmental Forensics 15(4):329–336.
  • Wang, S. C. 2003. Artificial neural network. The Springer International Series in Engineering and Computer Science 743:81–100.
  • Wei, Q., Wu, J., Zhang, Y., et al. 2019. Short-term exposure to sulfur dioxide and the risk of childhood hand, foot, and mouth disease during different seasons in Hefei, China. The Science of the Total Environment 658:116–121.
  • Xie, H., Tang, H., and Liao, Y. H. 2009. Time series prediction based on NARX neural networks: An advanced approach. 2009 International Conference on Machine Learning and Cybernetics 3:1275–1279.
  • Zounemat-Kermani, M. 2014. Principal component analysis (PCA) for estimating chlorophyll concentration using forward and generalized regression neural networks. Applied Artifical Intelligence 28(1):16–29.
  • Zounemat-Kermani, M., Stephanb, D., and Hinkelmannc, R. 2019. Multivariate NARX neural network in prediction gaseous emissions within the influent chamber of wastewater treatment plants. Atmospheric Pollution Research 10:1812–1822.

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