Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo,Italy |
| |
| Institution: | 1. Dipartimento di Ricerche Energetiche ed Ambientali (DREAM), Università di Palermo, Viale delle Scienze, Edificio 9, 90128, Palermo, Italy;2. Dipartimento Ingegneria Informatica (DINFO), Università di Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy;3. Dipartimento di Biotecnologie Mediche e Medicina Legale (DIBIMEL), Università di Palermo, Via del Vespro, 139, 90127, Palermo, Italy;1. Dept. of Science Education, Ewha Womans University, Seoul, Republic of Korea;2. Dept. of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea;3. Universities Space Research Association, Columbia, MD, USA;4. Dept. of Atmospheric and Environmental Sciences, Gangneung-Wonju National University, Gangneung, Republic of Korea;5. Dept. of Chemistry, Howard University, Washington, DC, USA;6. JCET, University of Maryland Baltimore County, Baltimore, MD, USA;7. NASA/Goddard Space Flight Center, Greenbelt, MD, USA;8. National Institute of Environmental Research, Inchon, Republic of Korea;1. Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China;2. Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou, 510301, China;3. University of Chinese Academy of Sciences, Beijing, 100049, China;4. School of Geographic Sciences, South China Normal University, Guangzhou, 510631, China;1. Center of Excellence CETEMPS, University of L''Aquila, Via Vetoio, 67010, Coppito, L''Aquila, Italy;2. Department of Physical and Chemical Sciences, University of L''Aquila, Via Vetoio, 67010, Coppito, L''Aquila, Italy;3. ARTA, Agenzia Regionale per l''Ambiente, Viale Marconi, Pescara, Italy;4. Department of Psychological, Health and Territorial Sciences, University “G. d''Annunzio” of Chieti-Pescara, Via dei Vestini, 31, 66100, Chieti, Italy;1. Department of Industrial Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran;2. Department of Industrial Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;3. Sustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic |
| |
| Abstract: | Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between 1 January 2003 to 31 December 2004 in eight monitoring stations in urban area of Palermo (Italy). Experimental trials show that the developed and tuned model is appropriate, giving small values of root mean square error (RMSE) , mean absolute error (MAE) and mean square error (MSE). In addition, the related correlation coefficient ranges from 0.72 to 0.97 for each forecasted pollutant, underlying a small difference between the forecasted and the measured values. The above results make the proposed forecaster a powerful tool for pollution management systems. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
