Skip to main content

Advertisement

Log in

A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction

  • Published:
Air Quality, Atmosphere & Health Aims and scope Submit manuscript

Abstract

Ozone, which is one of the most crucial pollutants regarding air quality and climate change, negatively impacts on human health, climate, and vegetation; therefore, the prediction of surface ozone concentration is very important in the protection of human health and environment. In this study, a convolutional neural networks and long short-term memory (CNN-LSTM) hybrid model that combines convolutional neural network (CNN), which can efficiently extract the inherent features of huge air quality and meteorological data, and long short-term memory (LSTM), which can sufficiently reflect the long-term historic process of the input time series data, was proposed and used for the ozone predictor to predict the next day’s 8-h average ozone concentration in Beijing City. At first, the number of historic data was set as 34 days via optimization, so that the input data suitable for the CNN-LSTM model to ensure the quick and precise prediction of ozone were constructed. In addition, the CNN-LSTM model candidates with different structures were proposed and used to construct the optimal model structure for the proposed ozone predictor. Finally, the performance of the proposed ozone predictor was evaluated and compared with multi-layer perceptron (MLP) and LSTM models; as a result, the performance indexes (RMSE, MAE, and MAPE) were reduced to 83% compared to the MLP model and 35% compared to the LSTM model. In conclusion, it was demonstrated that the proposed CNN-LSTM hybrid model has the satisfactory seasonal stability and the prediction performance superior to MLP and LSTM models.

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

Access this article

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Baklanov A, Mestayer PG, Clappier A, Zilitinkevich S, Joffre S, Mahura A, Nielsen NW (2008) Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description. Atmos Chem Phys 8(3):523–543. https://doi.org/10.5194/acp-8-523-2008

    Article  CAS  Google Scholar 

  • Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  Google Scholar 

  • Bernstein JA, Alexis N, Barnes C, Bernstein IL, Bernstein JA, Nel A, Peden D et al (2004) Health effects of air pollution. J Allergy Clin Immunol 114(5):1116–1123. https://doi.org/10.1016/j.jaci.2004.08.030

    Article  Google Scholar 

  • Biancofiore F, Verdecchia M, Carlo PD, Tomassetti B, Aruffo E, Busilacchio M, Bianco S, Tommaso SD, Colangeli C (2015) Analysis of surface ozone using a recurrent neural network. Sci Total Environ 514:379–387

    Article  CAS  Google Scholar 

  • Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360(9341):1233–1242

    Article  CAS  Google Scholar 

  • Carlo PD, Pitari G, Mancini E, Gentile S, Pichelli E, Visconti G (2007) Evolution of surface ozone in central Italy based on observations and statistical model. J Geophys Res 112:D10316. https://doi.org/10.1029/2006JD007900

    Article  CAS  Google Scholar 

  • Castellano M, Franco A, Cartelle D, Febrero M, Roca E (2009) Identification of NOx and ozone episodes and estimation of ozone by statistical analysis. Water Air Soil Pollut 198:95–110

    Article  CAS  Google Scholar 

  • Chaloulakou A, Saisana M, Spyrellis N (2003) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci Total Environ 313:1–13

    Article  CAS  Google Scholar 

  • Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032. https://doi.org/10.1109/TIP.2015.2475625

    Article  Google Scholar 

  • Chattopadhyay S, Bandyopadhyay G (2007) Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland. Int J Remote Sens 28(20):4471–4482. https://doi.org/10.1080/01431160701250440

    Article  Google Scholar 

  • Chattopadhyay S, Chattopadhyay G (2012) Modeling and prediction of monthly total ozone concentrations by use of an artificial neural network based on principal component analysis. Pure Appl Geophys 169(10):1891–1908

    Article  Google Scholar 

  • Chen Y, Shi R, Shu S, Gao W (2013) Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis. Atmos Environ 74:346–359. https://doi.org/10.1016/j.atmosenv.2013.04.002

    Article  CAS  Google Scholar 

  • Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning. ACM, New York, pp 160–167

  • Dutot AL, Rynkiewicz J, Steiner FE, Rude J (2007) A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions. Environ Model Softw 22(9):1261–1269

    Article  Google Scholar 

  • Esplin GJ (1995) Approximate explicit solution to the general line source problem. Atmos Environ 29(12):1459–1463

    Article  CAS  Google Scholar 

  • Faris H, Alkasassbeh M, Rodan A (2014) Artificial neural networks for surface ozone prediction: models and analysis. Pol J Environ Stud 23(2):341–348

    CAS  Google Scholar 

  • Gorai AK, Mitra G (2017) A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration. Air Qual Atmos Health 10(2):213–223. https://doi.org/10.1007/s11869-016-0417-0

    Article  CAS  Google Scholar 

  • Gorai AK, Tuluri F, Tchounwou PB, Ambinakudige S (2015) Influence of local meteorology and NO 2 conditions on ground-level ozone concentrations in the eastern part of Texas, USA. Air Qual Atmos Health 8(1):81–96. https://doi.org/10.1007/s11869-014-0276-5

    Article  CAS  Google Scholar 

  • Goyal P, Chan AT, Jaiswal N (2006) Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmos Environ 40(11):2068–2077. https://doi.org/10.1016/j.atmosenv.2005.11.041

    Article  CAS  Google Scholar 

  • Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  Google Scholar 

  • Hubbard MC, Cobourn WG (1998) Development of a regression model to forecast ground-level ozone concentration in Louisville, KY. Atmos Environ 32:2637–2647

    Article  CAS  Google Scholar 

  • Kim Y, Fu JS (2010) Improving ozone modeling in complex terrain at a fine grid resolution: part I—examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model. Atmos Environ 44(4):523–532

    Article  CAS  Google Scholar 

  • Ko B (2018) A brief review of facial emotion recognition based on visual information. Sensors 18(2):401

    Article  Google Scholar 

  • Li X, Peng L, Hu Y, Shao J, Chi T (2016) Deep learning architecture for air quality predictions. Environ Sci Pollut Res 23:22408–22417. https://doi.org/10.1007/s11356-016-7812-9

    Article  Google Scholar 

  • Mohamed AR, Sainath TN, Dahl G, Ramabhadran B, Hinton GE, Picheny MA (2011) Deep belief networks using discriminative features for phone recognition. In: 2011 I.E. International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/ICASSP.2011.5947494

  • Ong BT, Sugiura K, Zettsu K (2014) Dynamic pre-training of deep recurrent neural networks for predicting environmental monitoring data. IEEE Int Conf Big Data 16(2):760–765. https://doi.org/10.1109/BigData.2014.7004302

    Article  Google Scholar 

  • Rasmussen DJ, Fiore AM, Naik V, Horowitz LW, McGinnis SJ, Schultz MG (2012) Surface ozone-temperature relationships in the eastern US: a monthly climatology for evaluating chemistry-climate models. Atmos Environ 47:142–153

    Article  CAS  Google Scholar 

  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489. https://doi.org/10.1038/nature16961

    Article  CAS  Google Scholar 

  • Solaiman TA, Coulibaly P, Kanaroglou P (2008) Ground-level ozone forecasting using data-driven methods. Air Qual Atmos Health 1(4):179–193. https://doi.org/10.1007/s11869-008-0023-x

    Article  CAS  Google Scholar 

  • Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22(1):97–103

    Article  Google Scholar 

  • Tarasova OA, Karpetchko AY (2003) Accounting for local meteorological effects in the ozone time-series of Lovozero (Kola Peninsula). Atmos Chem Phys 3(4):941–949

    Article  CAS  Google Scholar 

  • Thompson ML, Reynolds J, Cox LH, Guttorp P, Sampson PD (2001) A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmos Environ 35(3):617–630

    Article  Google Scholar 

  • WHO (2003) Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide. Tech. Rep., WHO

  • Wilson RC, Fleming ZL, Monks PS, Clain G, Henne S, Konovalov IB, Szopa S, Menut L (2012) Have primary emission reduction measures reduced ozone across Europe? An analysis of European rural background ozone trends 1996–2005. Atmos Chem Phys 12(1):437–454

    Article  CAS  Google Scholar 

  • Zhang CY, Chen CLP, Gan M, Chen L (2015) Predictive deep Boltzmann machine for multiperiod wind speed forecasting. IEEE Trans Sustain Energy 6(4):1416–1425. https://doi.org/10.1109/TSTE.2015.2434387

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the Ministry of Ecology and Environment, People’s Republic of China, and Shanghai 2345 Network Technology Co., Ltd. for providing the experiment data for pursuing the work. The critical reading of the manuscript by the anonymous reviewer is greatly appreciated.

Funding

This study was supported by a grant from the Environmental Science and Engineering Research Council, Democratic People’s Republic of Korea.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chungsong Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pak, U., Kim, C., Ryu, U. et al. A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Qual Atmos Health 11, 883–895 (2018). https://doi.org/10.1007/s11869-018-0585-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11869-018-0585-1

Keywords

Navigation