Abstract
Tabriz as one of the major industrial cities of Iran is not immune to air pollution and spends many days air-polluted each year. Since one of the goals of sustainable development is to achieve clean and good air for all segments of society and to attract clean air from the citizens of a city. In this study, it was attempted to present an efficient model for predicting CO pollutant concentrations using artificial neural network (ANN) and adaptive neural-fuzzy inference system (ANFIS). Air quality monitoring and developing efficient air pollution models can be suitable in providing requirements for sustainable development goals. For modelling, the meteorological and pollutant data were first obtained from the Meteorological and Environmental Agency of Tabriz city. The model inputs were temperature, wind speed, humidity and contaminant concentration daily hour index and weekly day index. The results of the study at validation step yielded 0.82 and 0.63 in terms of determination coefficient for ANN models and ANFIS models. It was also observed that the Ensemble method worked even better than the single 2 methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Afuni M, Erfanmanesh M, (2009) Environmental pollution: water, soil and air. Publication of the pillars of knowledge. 8: 219–220 (in Persian). https://www.adinehbook.com/gp/product/9646227929.
Giustolisi O, Doglioni A, Savic DA, Webb BW (2007) A multi-model approach to analysis of environmental phenomena. Environ Model Softw 22:674–682. https://doi.org/10.1016/j.envsoft.2005.12.026
Hrust L, Klaić ZB, Križan J, Antonić O, Hercog P (2009) Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos Environ 43(35):5588–5596. https://doi.org/10.1016/j.atmosenv.2009.07.048
https://www.worldenvironmentday.global/air-pollution-and-sustainable-development-goals
Jedi H, Abbaspour R, Khalesian M, Alawi Panah K (2017) Prediction of carbon monoxide concentration in Tehran metropolis using artificial neural networks. Q J Environ Sci Technol 19:13–25 (in Persian). http://jest.srbiau.ac.ir/article_11214.html
Khazaei E, Al Sheikh A, Karimi M, Vahidnia M (2011) Comparison of two modeling methods using neural-fuzzy network in predicting carbon monoxide pollutant concentration. Environ Stud 38(64):29–44 (in Persian). https://www.sid.ir/fa/journal/ViewPaper.aspx?id=182493
Kurt A, Oktay AB (2010) Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst Appl 37(12):7986–7992. https://doi.org/10.1016/j.eswa.2010.05.093
Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241. https://doi.org/10.1029/1998WR900018
Mihalache S, Popescu M, Oprea M, (2015) Particulate matter prediction using ANFIS modelling techniques. In 2015 19th International Conference on System Theory, Control and Computing (ICSTCC). 895–900. https://doi.org/https://doi.org/10.1109/ICSTCC.2015.7321408
Nourani V (2017) An emotional ANN (EANN) approach to modeling rainfall-runoff process. J Hydrol 544:267–277. https://doi.org/10.1016/j.jhydrol.2016.11.033
Oprea M, Popescu M, Mihalache SF, Dragomir EG (2017) Data mining and ANFIS application to particulate matter air pollutant prediction. A comparative study. In ICAART (2): 551–558. https://www.scitepress.org/papers/2017/61964/61964.pdf
Ravandi Paydar S (2001) Assessment of air pollution and physical factor in the electrical industry. 3rd International Congress on Health and Environment, Iran, Kerman (in Persian). https://civilica.com/doc/78540/
Saeedi M, Karbassi A, Sohrabi T, Samadi R (2005) Environmental management of powerhouses. Department of Environmental Planning Bureau of Energy Affairs. Ministry of Energy, Iran (in Persian). http://saba.org.ir/saba_content/media/image/2012/04/3583_orig.pdf
Zangui H, Assadollah Fardi G (2017) Prediction of PM10 air pollution in Mashhad using MLP artificial neural networks and Markov chain model. J Appl Res Geogr Sci 7:39–59 (in Persian). https://jgs.khu.ac.ir/article-1-2859-fa.pdf
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is a part of the Topical collection in Environmental Earth Sciences on “Water Problems in E. Mediterranean Countries” guest edited by H. Gökçeku, D. Orhon, V. Nourania, and S. Sozen.
Rights and permissions
About this article
Cite this article
Nourani, V., Karimzadeh, H. & Baghanam, A.H. Forecasting CO pollutant concentration of Tabriz city air using artificial neural network and adaptive neuro-fuzzy inference system and its impact on sustainable development of urban. Environ Earth Sci 80, 136 (2021). https://doi.org/10.1007/s12665-021-09423-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-021-09423-x