Skip to main content
SpringerLink
Log in
Book cover

Emerging Research in Data Engineering Systems and Computer Communications pp 353–365Cite as

Review on Neural Network Algorithms for Air Pollution Analysis

  • Conference paper
  • First Online:

  • 1002 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1054))

Abstract

Neural network is a layer-based optimization technique to solve a real-time problem adjusting the weight values of the neuron based on its activation function. It aids to construct a model to compute optimum results in business analytical process, prediction analysis, financial forecasting, environmental analysis, etc. The environmental analysis are having two approaches namely determine the pollution or identifying the quality using environmental factors such as air, water, and land. The air pollution analysis and predication is to control the pollution. It is a challenging process due to its computational complexity. The environmental research community is working on air pollution factor analysis, pollution index computation, and predication. Present research addresses the findings of various artificial neural network algorithms and presented same. It is recognized that the obtained neural network models are providing sufficient reliable forecast that indicates an effective tool for analyzing and predicting the air pollution. Thus, the study aims to provide various ongoing research results of air pollution analysis and presented the usage of artificial neural network for analysis and prediction of air pollution.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Benvenuto, F., Marani, A.: Neural networks for environmental problems: data quality control and air pollution nowcasting. Glob. NEST Int. J. 2(3), 281–292 (2000)

    Google Scholar 

  2. Lungu, E.: Development of a short-medium forecasting system for air pollution (in Romanian). Postdoctoral final research report, University Petroleum—Gas of Ploiesti, Department of Informatics (2007 Oct)

    Google Scholar 

  3. Oprea, M.: A case study of knowledge modelling in an air pollution control decision support system. In: AI Communications, IOS Press, vol. 18, No. 4 (2005)

    Google Scholar 

  4. Russo, A., Soares, A.O.: Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach. © International Association for Mathematical Geosciences (2013 July)

    Google Scholar 

  5. LeCun, Y., Bengio, Y., Hinton, G.: Nature. Deep Learn. 521(7553), 436–44 (2015 May 28)

    Google Scholar 

  6. Hemlata, K., Usha Rani, K.: Advancements in multi-layer perceptron training to improve classification accuracy. Int. J. Recent Innov. Trends Comput. Commun. 5(6), 353–357 (2017 June). ISSN: 2321-8169

    Google Scholar 

  7. Jamal, H.H., Pillay, M.S., Zailina, H., Shamsul, B.S., Sinha, K., Zaman Huri, Z., Khew, S.L., Mazrura, S., Ambu, S., Rahimah, A., Ruzita, M.S.: A study of health impact & risk assessment of urban air pollution in Klang Valley. UKM Pakarunding Sdn Bhd, Kuala Lumpur (2004)

    Google Scholar 

  8. Kamal, M.M., Jailani, R., Shauri, R.L.A.: Prediction of ambient air quality based on neural network technique. In: 4th Student Conference on Research and Development, Selangor, 27–28 June 2006

    Google Scholar 

  9. Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J., Kolehmainen, M.: Methods for imputation of missing values in air quality data set. Atmos. Environ. 38 (2004)

    Google Scholar 

  10. Nasir, M.F.M., Juahir, H., Roslan, N., Mohd, I., Shafie, N.A., Ramli, N.: Artificial neural networks combined with sensitivity analysis as a prediction model for water quality index in Juru River, Malaysia. Int. J. Environ. Protect. 1(3) (2011). http://dx.doi.org/10.5963/IJEP0103001

  11. National Weather Service Corporate Image Web Team: NOAA’s National Weather Service/Environmental Protection Agency—United States Air Quality Forecast Guidance. Retrieved 20 August 2015

    Google Scholar 

  12. Ott, W.R.: Environmental Indices: Theory and Practice. Ann Arbor Science Publishers Inc., Ann Arbor, Michigan, USA (1978)

    Google Scholar 

  13. Environment Protection Agency (EPA): USAir Pollution Index. Retrieved 20 Aug 2018. https://www3.epa.gov/airnow/aqi_brochure_02_14.pdf

  14. Azid, A., Juahir, A., Latif, M., Zain, S., Osman, M.: Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia. J. Environ. Protect. 4(12A), 1–10 (2013 Dec). https://doi.org/10.4236/jep.2013.412a1001

  15. Marvin, H.: Green an air pollution index based on sulfur dioxide and smoke shade. J. Air Pollut. Control Assoc. 16(12), 703–706 (1966). https://doi.org/10.1080/00022470.1966.10468537

    Article  Google Scholar 

  16. Fensterstock, J.C., Goodman, K., Duggan, G.M., Baker, W.S.: The development and utilization of an air quality index. In: Proceedings of 62nd Annual Meeting of the APCA, New York, 1969; Paper 69–73 [15] (2004 Nov)

    Google Scholar 

  17. Siwek, K., Osowskia, S.: Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Eng. Appl. Artif. Intell. Eng. Appl. Artif. Intell. 25(6) (2012 Sept)

    Google Scholar 

  18. Rahmana, N.H.A., Leea, M.H., Latifb, M.T., Suhartonoc, S.: Forecasting of air pollution index with artificial neural network. J. Teknol. (2013). eISSN 2180–3722 | ISSN 0127–9696

    Google Scholar 

  19. Rahman, P.A., Panchenko, A.A., Safarov, A.M.: Using neural networks for prediction of air pollution index in industrial city. In: IOP Conference Series: Earth and Environmental Science (2016). https://doi.org/10.1088/1755-1315/87/4/04

  20. Bai, L., Wang, J., Ma, X., Lu, H.: Air pollution forecasts: an overview. Int. J. Environ. Res. Public Health (2018). https://doi.org/10.3390/ijerph15040780

    Article  Google Scholar 

  21. Asghari Esfandani, M., Nematzadeh, H.: Predicting air pollution in Tehran: genetic algorithm and back propagation neural network. J. AI Data Min. 4(1), 49–54 (2016). https://doi.org/10.5829/idosi.jaidm.2016.04.01.06, (2015)

  22. Narasimha Reddy, V., Mohanty, S.: Deep Air: Forecasting Air Pollution in Beijing, China (2017)

    Google Scholar 

  23. Barai, S.V., Dikshit, A.K., Sharma, S.: Neural Network Models for Air Quality Prediction: A Comparative Study (2007). https://doi.org/10.1007/978-3-540-70706-6_27

  24. Catalano, M., Galatioto, F., Bell, M., Namdeo, A., Bergantino, A.S.: Improving the prediction of air pollution peak episodes generated by urban transport networks. Environ. Sci. Policy 60 (2016 June)

    Google Scholar 

  25. Antanasijević, D.Z., Pocajt, V.V., Povrenović, D.S., Ristić, M.Đ., Perić-Grujić, A.A.: PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ. 443, 511–519 (2013). ISSN 0048-9697

    Google Scholar 

  26. Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J.: Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107, 118–128 (2015). ISSN 1352-2310

    Google Scholar 

  27. Li, X., Song, J.: A new ANN-Markov chain methodology for air quality prediction. In: International Joint Conference on Neural Networks, pp. 12–17, July, 2015

    Google Scholar 

  28. Ma, L., Xin, K., Liu, S.: Using radial basis function neural networks to calibrate air quality model. World Acad. Sci. Eng. Technol. Int. J. Environ. Chem. Ecolog. Geolog. Geophys. Eng. 2(2) (2008)

    Google Scholar 

  29. Aggarwal, S.H., Khare, K.: Predictive analysis of air quality parameters using deep learning. Int. J. Comput. Appl. 125(9), 0975–8887 (2015). Access from Google Scholar, Sept. 2015

    Google Scholar 

  30. Jaloree, S., Rajput, A., Gour, S.: Decision tree approach to build a model for air quality. Bin. J. Data Min. Netw. 4(1) (2014)

    Google Scholar 

  31. Liao, H., Sun, W.: Forecasting and evaluating air quality of Chao Lake based on an improved decision tree method. Procedia Environ. Sci. 2 (2010)

    Google Scholar 

  32. Yan-jun, L., Qian, M.: AP-LSSVM modeling for air quality prediction. In: Control Conference (CCC), 2012 31st Chinese. IEEE, New York (2012)

    Google Scholar 

  33. de Gennaro, G., Trizio, L., Di Gilio, A., Pey, J., Pérez, N., Cusack, M., Alastuey, A., Querol, X.: Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean. Sci. Total Environ. (2013 July)

    Google Scholar 

  34. Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanem, J., Junninen, H., Kolehmainem, M., Niska, H., Dorling, S., Chartenton, T., Foxall, R., Cawley, G.: Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentration, compared with a deterministic modelling system and a measurements in central Helsinki. Atmos. Environ. 37, 4539–4550 (2003)

    Article  Google Scholar 

  35. Khan, Y., See, C.S.: Predicting and analyzing air quality using machine learning: a comprehensive model. In: IEEE Long Island Systems, Applications and Technology Conference (LISAT) (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati, Andhra Pradesh, India

    Sumaya Sanober & K. Usha Rani

Authors
  1. Sumaya Sanober
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Usha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sumaya Sanober .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Sri Padmavati Mahila University, Tirupathi, Andhra Pradesh, India

    P. Venkata Krishna

  2. Department of ECE, Nazarbayev University, Astana, Kazakhstan

    Mohammad S. Obaidat

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sanober, S., Usha Rani, K. (2020). Review on Neural Network Algorithms for Air Pollution Analysis. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_34

Download citation

Publish with us

Policies and ethics

Search

Navigation