Skip to main content
SpringerLink
Log in

An Improved Activation Function in Convolution Neural Network to Estimate the Hazardous Air Pollutant Based on Images

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

This article has been updated

Abstract

This study addresses the challenge of accurately estimating air pollution levels, which pose significant health, environmental, and economic risks. Variations in air quality across different regions, with urban and power plant areas typically experiencing higher pollution levels, highlight the need for effective monitoring methods beyond traditional sensor-based approaches. This study proposes a method to estimate air pollution levels from images using a Convolution Neural Network (CNN) model, aiming to overcome the limitations of traditional monitoring stations. The proposed method leverages the Resnet-152 architecture to accurately estimate Particulate Matter (PM2.5) concentrations, benefiting from its implicit and invariant distortion features tailored for particulate matter modeling. Hyperparameter tuning during image training and using max-pooling layers with three kernels and one stride helps mitigate overfitting issues. The application of max-group layers facilitates the extraction of relevant information from activation maps, enhancing estimation precision. The Resnet-152 architecture with fewer parameters and invariant distortion characteristics, accelerates particulate matter estimation. Experimental results demonstrate the effectiveness of the proposed method, with a root mean square error (RMSE) of 0.10 and a mean absolute percentage error (MAPE) of 19.38%, outperforming other models such as Inception-v3, VGG-19, and Googlenet, thus showcasing its potential for practical air pollution monitoring applications.

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

Similar content being viewed by others

Data Availability

Not applicable.

Change history

  • 22 May 2024

    The original online version of this article was revised: In this article the affiliation details for the author were incorrectly given as 'Komeru Lakshmaiah Education Foundation' but should have been 'Koneru Lakshmaiah Education Foundation'. The original article has been corrected.

References

  1. Mcginnis, J. M., & William, H. F. (1993). Actual causes of death in the united states. The Journal of the American Medical Association, 270(18), 2207–2212.

    Article  Google Scholar 

  2. Cheng, Y., Kebin, H., Zhen, V. D., Mei, Z., Fengkui, D., & Yong, L. M. (2015). Humidity plays an important role in the PM2.5 pollution in Beijing. Environmental Pollution, 197, 68–75.

    Article  Google Scholar 

  3. Health Effect Institute. (2019). Retrieved 26 Dec 2020 from www.healtheffects.org

  4. Guttikonda, S. (2017) Air pollution in Indian Cities: Understanding the causes and the knowledge gaps, Centre for policy research.

  5. World’s most polluted cities in 2019 PM2.5 ranking—Air Visual. (2019). Empowering the world to breathe cleaner air—IQ Air. Retrieved 12 Jan 2022.

  6. Chandra, P., & Singh, Y. (2004). An activation function adapting training algorithm for sigmoidal feedforward networks. Neurocomputing, 61, 429–4379.

    Article  Google Scholar 

  7. Duch, W., & Jankowski, N. (1999). Survey of neural transfer functions. Neural Computing Surveys, 2, 163–212.

    Google Scholar 

  8. Duch, W., Jankowski, N. (2001). Transfer functions: Hidden possibilities for better neural networks. In 9th European symposium on artificial neural networks (pp. 81–94).

  9. Singh, Y., & Chandra, P. A. (2003). Class1 sigmoidal activation functions for FFANNs. Journal of Economic Dynamics and Control, 28(1), 183–187.

    Article  MathSciNet  Google Scholar 

  10. Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S. (2016). Deep residual learning for image recognition. In IEEE International conference on computer vision and pattern recognition (pp. 770–778).

  11. Li, Y., Huang, J., Luo, J. (2015). Using user generated online photos to estimate and monitor air pollution in major cities. In Proceedings of the 7th International Conference on Internet Multimedia Computing and Service (pp. 1–5).

  12. Mao, J., Uthai, P., Shinava, W., & Hirovuki, S. (2014). Detecting foggy images and estimating the haze degree factor. Journal of computer science and systems Biology, 6, 1–10.

    Google Scholar 

  13. Liu, C., Tsow, F., Zou, Y., & Tao, N. (2016). Particle pollution estimation based on image analysis. PLoS ONE, 11(2), e0145955.

    Article  Google Scholar 

  14. Schultz, A. A., Schauer, J. J., & Malecki, K. M. (2017). Allergic disease associations with regional and localized estimates of air pollution. Environmental Research, 1(155), 77–85.

    Article  Google Scholar 

  15. Mousavi, S. E., Heydarpour, P., Reis, J., Amiri, M., & Sahraian, M. A. (2017). Multiple sclerosis and air pollution exposure: Mechanisms toward brain autoimmunity. Medical Hypotheses, 1(100), 23–30.

    Article  Google Scholar 

  16. Barrea, L., Savastano, S., Di Somma, C., Savanelli, M. C., Nappi, F., Albanese, L., Orio, F., & Colao, A. (2017). Low serum vitamin D-status, air pollution and obesity: A dangerous liaison. Reviews in Endocrine and Metabolic Disorders, 18, 207–214.

    Article  Google Scholar 

  17. Wacker, M., & Holick, M. F. (2013). Sunlight and vitamin D: A global perspective for health. Dermato-Endocrinology, 5(1), 51–108.

    Article  Google Scholar 

  18. Procházka, A., Kolinova, M., Fiala, J., Hampl, P., Hlavaty, K. (2000). Satellite image processing and air pollution detection. In 2000 IEEE international conference on acoustics, speech, and signal processing. Proceedings (cat. No. 00CH37100) (Vol. 4, pp. 2282–2285). IEEE.

  19. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advanced Neural Information Processing System, 25, 1097–1105.

    Google Scholar 

  20. Zhang, C., Yan, J., Li, C., Rui, X., Liu, L., Bie, R. (2016). On estimating air pollution from photos using convolutional neural network. In ACM International conference on Multimedia (pp. 297–301).

  21. Chakma, A., Vizena, B., Cao, T., Lin, J., Zhang, J. (2017). Image based air quality analysis using deep convolutional neural network. In International conference on Image Processing (pp. 3949–3952). IEEE.

  22. Lu, C., Lin, D., Jia, J., & Tang, C. (2017). Two class weather classification. IEEE Transaction Pattern Analysis Machine Intelligence, 39, 2510–2524.

    Article  Google Scholar 

  23. Elhoseiny, M., Huang, S., Elgammal, A. (2015). Weather classification with deep convolution neural networks. In International Conference of on Image Processing (pp. 3349–3353).

  24. Liu, F., Shen, C., Lin, G. (2015). Deep convolutional neural fields for depth estimation from a single image. In International conference on CVPR (pp. 5162–5170).

  25. He, K., Sun, J., & Tang, X. (2011). Single image haze removal using dark channer prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2341–2353.

    Article  Google Scholar 

  26. Jehong, A., Yunfan, C., Hyunchul, S. (2018). Weather classification usingconvolution neural networks. In International SOC design conference (ISOCC2018) (pp. 245–246).

  27. Li, L., Zheng, Y., Zhang, L. (2014). Demonstration abstract: PiMi air box—A cost-effective sensor for participatory indoor quality monitoring. In International Symposium on information processing in sensor networks (pp. 327–328).

  28. Christoph, G., Erik Rodner, Alexander, F., Joachim, D. (2014). Non-parametric part transfer for fine-grained recognition. In IEEE International conference on computer vision and pattern recognition (pp. 2489–2496).

  29. Andrea, V., Brian, F. (2010). VLFeat: An open and portable library of computer vision algorithms. In ACM International conference on Multimedia (pp. 1469–1472).

  30. Xing, H., Wang, F., Liu, C., & Suo, M. (2021). PM2.5 concentration modelling and prediction by using temperature based deep belief network. Neural Networks, 133, 157–165.

    Article  Google Scholar 

  31. Liu, H., Li, F., Xu, F., & Lu, H. (2011). The evaluation of air quality using image quality. China Journal of Image Graph, 16, 1030–1037.

    Google Scholar 

  32. Song, Y. Z., Yang, H. L., Peng, J. H., Song, Y. R., Sun, Q., & Li, Y. (2015). Estimating PM2.5 concentrations in Xian city using a generalized additive model with multi-source monitoring data. PLoS ONE, 10, 1–15.

    Article  Google Scholar 

  33. Abbey, D. E., Ostro, B. E., Fraser, G., Vancuren, T., & Burchette, R. J. (1995). Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) from airport visibility data in California. Journal of Exposure Analysis and Environmental Epidemiology, 5(2), 161–180.

    Google Scholar 

  34. Wang, J. L. (2006). Quantiative relationship between visibility and mass concentration of PM2.5 in Beijing. Journal of Environmantal Science, 18(3), 475–481.

    Google Scholar 

  35. Gu, K., Qiao, X., & Li, X. (2019). Highly effiecient picture based prediction of PM2.5 concentration. IEEE Transactions Industrial Electronics, 66, 3176–3184.

    Article  Google Scholar 

  36. Zhang, H., Peng, D., Chen, W., & Xu, X. (2019). Extremely efficient PM2.5 estimator based on analysis of sailency and statistics. Electronics Letters, 55, 30–32.

    Article  Google Scholar 

  37. Kezheng, S., Lijuan, T., Jiansheng, Q., Guangcheng, W., & Cairong, L. (2021). A deep learning based PM2.5 concentration estimator. Displays, 69, 1–6.

    Google Scholar 

  38. Predic, B., Yan, Z., Eberle, J., Stojanovic, D., Aberer, K. (2013). Exposuresense: Integrating daily activities with air quality using mobile participatory sensing. In IEEE PERCOM workshop (pp. 303–305).

  39. Nikzad, N., Verma, N., Ziftci, C., Bales, E., Quick, N., Zappi, K. et al. (2012). Citisense: Improving geospatial environmental assessment of air quality using a wireless personal exposure monitring system. In ACM conference on wireless health (pp. 11.1–11.8).

  40. Kim, K. W., & Kim, Y. J. (2005). Perceived visibility measurement using the HSI color difference method. Journal of the Korean Physical Society, 46(5), 1243–1250.

    Google Scholar 

  41. Zhan, Y., Zhang, R., Wu, Q., Wu, Y. (2016). A new haze image database with detailed air quality information and a novel no-reference image quality assessment method for haze images. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 1095–1099). IEEE.

  42. Steininger, M., Kobs, K., Zehe, A., Lautenschlager, F., Becker, M., & Hotho, A. (2020). Maplur: Exploring a new paradigm for estimating air pollution using deep learning on map images. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(3), 1–24.

    Article  Google Scholar 

  43. Andhra Pradesh Central Pollution Control Board (APPCB). Air Quality Status of Andhra Pradesh. http://aprtpms.ap.gov.in/publicview.html

  44. Allison JS, Betsch S, Ebner B, Visagie J. (2022). On testing the adequacy of the inverse Gaussian distribution. Mathematics, 10(3), 350.

  45. Mingjie, H., Jie, Z., Shiguang, S., Meina, K., Xillin, C. (2019). Deformable Facenet: Learning pose invariant feature with pose aware feature alignment for face recognition. In IEEE International Conference on Automatic Face and gesture recognition (pp. 1–10).

  46. Enas, E., Awny, S., Ahmed, R. G., & Alaa, M. Z. (2021). Hyperparameter tuning for machine learning algorithms for Arabic sentiment analysis. Informatics, 8(4), 1–13.

    Google Scholar 

  47. Saad, A., Tareq, A. M., Saad, A. Z. (2017). Understanding of a convolutional neural network. In International Conference on Engineering and Technology (pp. 1–15). IEEE.

Download references

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Usharani Bhimavarapu

Authors
  1. Usharani Bhimavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Usharani Bhimavarapu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Bhimavarapu, U. An Improved Activation Function in Convolution Neural Network to Estimate the Hazardous Air Pollutant Based on Images. Wireless Pers Commun 135, 2401–2420 (2024). https://doi.org/10.1007/s11277-024-11174-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11174-4

Keywords

Search

Navigation