Skip to main content
SpringerLink
Log in

Improving artificial intelligence-based air pollution modeling with the application of meteorological data

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

Emotional artificial neural network (EANN) a new generation of artificial neural network linked to the wavelet-based data preprocessing method, was used in this study to predict air pollution in Tabriz city, Iran, from 2015 to 2019. For comparison purposes, the classic feedforward neural network (FFNN) model was applied. Daily meteorological data, as well as pollutants concentration data, were used to predict air pollution concentration in the future. The results showed the efficiency of the EANN model in comparison with classic FFNN in predicting air pollution for Tabriz city. Also, the wavelet EANN (WEANN) model outperformed the wavelet FFNN (WFFNN) and EANN models by up to 4 and 14%, respectively. This result denotes the importance of preprocessing method than applying more updated artificial intelligence methods. The superiority of the proposed WEANN approach is in better learning of extraordinary and extreme values in the validation phase, due to the improved ability of EANN via hormones in comparison with ANN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

EANN:

Emotional artificial neural network

FFNN:

Feedforward neural network

WEANN:

Wavelet EANN

WFFNN:

Wavelet FFNN

WHO:

World Health Organization

AI:

Artificial intelligence

ARIMA:

Auto-regressive integrated moving average

RMSE:

Root mean square error

DC:

Determination coefficient

References

  • Abraham A, Nath B (2000) Hybrid intelligent systems design: a review of a decade of research. IEEE transactions on systems, man & cybernetics (Part-C) August

  • Addison PS, Murray K, Watson J (2001) Wavelet transform analysis of open channel wake flows. J Eng Mech 127(1):58. https://doi.org/10.1061/(ASCE)0733-9399

    Article  Google Scholar 

  • Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213

    Article  CAS  Google Scholar 

  • Bai Y, Li Y, Wang X, Xie J, Li C (2016) Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos Pollut Res 7(3):557–566

    Article  Google Scholar 

  • Berenji HR, Khedkar P (1992) Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans Neural Netw 3(5):724–740

    Article  CAS  Google Scholar 

  • Cabaneros SM, Calautit JK, Hughes BR (2019) A review of artificial neural network models for ambient air pollution prediction. Environ Model Softw 119:285–304

    Article  Google Scholar 

  • Czogala E, Leski J (2000) Fuzzy and neuro-fuzzy intelligent systems. Physica-Verlag Springer, pp 65–92

    Book  Google Scholar 

  • Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128

    Article  CAS  Google Scholar 

  • Ghorani-Azam A, Riahi-Zanjani B, Balali-Mood M (2016) Effects of air pollution on human health and practical measures for prevention in Iran. J Res Med Sci 21:65. https://doi.org/10.4103/1735-1995.189646

    Article  CAS  Google Scholar 

  • Guo Q, He Z, Li S, Li X, Meng J, Hou Z, Liu J, Chen Y (2020) Air pollution forecasting using artificial and wavelet neural networks with meteorological conditions. Aerosol Air Qual Res 20:1429–1439. https://doi.org/10.4209/aaqr.2020.03.0097

    Article  CAS  Google Scholar 

  • He J, Yu Y, Xie Y, Mao H, Wu L, Liu N, Zhao S (2016) Numerical model-based artificial neural network model and its application for quantifying impact factors of urban air quality. Water Air Soil Pollut 227(7):235

    Article  Google Scholar 

  • 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 concentra-tions. Atmos Environ 43(35):5588–5596

    Article  CAS  Google Scholar 

  • Jeddi H, Abbaspour R, Khalesian M, Alawi Panah K (2017) Prediction of carbon monoxide concentration in Tehran metropolis using artificial neural net-works. Q J Environl Sci Technol 19:13–25 ((in Persian))

    Google Scholar 

  • Khashman A (2008) A modified backpropagation learning algorithm with added emotional coefficients. IEEE Trans Neural Netw 19(11):1896–1909

    Article  Google Scholar 

  • Kurt A, Oktay AB (2010) Forecasting air pollutant indicator levels with geo-graphic models 3 days in advance using neural networks. Expert Syst Appl 37(12):7986–7992

    Article  Google Scholar 

  • Legates DR, McCabe GJ (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

    Article  Google Scholar 

  • Lei L, Chen W, Xue Y, Liu W (2019) A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network. Build Environ 162:106296

    Article  Google Scholar 

  • Liu H, Yin S, Chen C, Duan Z (2020) Data multi-scale decomposition strategies for air pollution forecasting: a comprehensive review. J Clean Prod 277:124023

    Article  Google Scholar 

  • Lotfi E, Akbarzadeh-T MR (2016) A winner-take-all approach to emotional neural networks with universal approximation property. Inf Sci 346:369–388

    Article  Google Scholar 

  • Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66

    Article  Google Scholar 

  • Nourani V (2017) An emotional ANN (EANN) approach to modeling rainfall-runoff process. J Hydrol 544:267–277

    Article  Google Scholar 

  • Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057

    Article  Google Scholar 

  • Nourani V, Molajou A, Uzelaltinbulat S, Sadikoglu F (2019) Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus. Theor Appl Climatol 138:1419–1434. https://doi.org/10.1007/s00704-019-02904-x

    Article  Google Scholar 

  • Roshni T, Jha MK, Drisya J (2020) Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Comput Applic 32:12737–12754

    Article  Google Scholar 

  • Schwartz J, Marcus A (1990) Mortality and air pollution j london: a time series analysis. Am J Epidemiol 131(1):185–194

    Article  CAS  Google Scholar 

  • Shekarrizfard M, Karimi-Jashni A, Hadad K (2012) Wavelet transform-based artificial neural networks (WT-ANN) in PM 10 pollution level estimation, based on circular variables. Environ Sci Pollut Res 19(1):256–268

    Article  CAS  Google Scholar 

  • Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1(1):67–71

    Google Scholar 

  • World Health Organization (2011) Database: outdoor air pollution in cities. Retrieved from http://www.who.int/phe/health_topics/outdoorair/databases/cities-2011/en/

  • Zhang C, Ding R, Xiao C, Xu Y, Cheng H, Zhu F, Lei R, Di D, Zhao Q, Cao J (2017) Association between air pollution and cardiovascular mortality in Hefei, China: a time-series analysis. Environ Pollut 229:790–797

    Article  CAS  Google Scholar 

  • Zhang J, Qiu H, Li X, Niu J, Nevers MB, Hu X, Phanikumar MS (2018) Real-Time nowcasting of microbiological water quality at recreational beaches: a wavelet and artificial neural network-based hybrid modeling approach. Environ Sci Technol 52(15):8446–8455

    Article  CAS  Google Scholar 

  • Zhu F, Ding R, Lei R, Cheng H, Liu J, Shen C, Zhang C, Xu Y, Xiao C, Li X, Zhang J (2019) The short-term effects of air pollution on respiratory diseases and lung cancer mortality in Hefei: a time-series analysis. Respir Med 146:57–65

    Article  Google Scholar 

Download references

Acknowledgment

The authors appreciate the meteorological and environmental organizations of East Azerbaijan province, Iran, for providing data for the current study.

Author information

Authors and Affiliations

  1. Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    A. H. Baghanam & H. Karimzadeh

  2. Center of Excellences in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    V. Nourani

  3. Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Cyprus

    V. Nourani

  4. College of Engineering, IT and Environment, Charles Darwin University, Darwin, Australia

    V. Nourani

Authors
  1. A. H. Baghanam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Karimzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. H. Baghanam.

Ethics declarations

Conflict of interest

There is no conflict

Additional information

Editorial responsibility: I. Akkurt.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baghanam, A.H., Nourani, V. & Karimzadeh, H. Improving artificial intelligence-based air pollution modeling with the application of meteorological data. Int. J. Environ. Sci. Technol. 21, 431–446 (2024). https://doi.org/10.1007/s13762-023-05273-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-023-05273-1

Keywords

Search

Navigation