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[1] Kowalski, P.A. & Warchałowski, W., The comparison of linear models for PM10 and PM2.5 forecasting, WIT Transaction on Ecology and the Environment, 230, WIT Press: Southampton and Boston, pp. 177–188, 2018.
[2] Domańska, D. & Wojtylak, M., Application of fuzzy time series models for forecasting pollution concentrations. Expert Systems with Applications, 39(9), pp. 7673–7679, 2012.
[3] Chakraborty, K., Mehrotra, K., Mohan, C.K. & Ranka, S., Forecasting the behavior of multivariate time series using neural networks. Neural Networks, 5(6), pp. 961–970, 1992.
[4] Faruk, D.O., A hybrid neural network and arima model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), pp. 586–594, 2010.
[5] Perez, P., Menares, C. & Ramirez, C., Forecasting in the Most Polluted City in South America, WIT Transaction on Ecology and the Environment, 230, WIT Press: Southampton and Boston, pp. 199–204, 2018.
[6] Grover, A., Kapoor, A. & Horvitz, E., A deep hybrid model for weather forecasting. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 379–386, 2015.
[7] Xie, J., Wang, X., Liu, Y. & Bai, Y., Autoencoder-based deep belief regression network for air particulate matter concentration forecasting. Journal of Intelligent & Fuzzy Systems, 34(6), pp. 3475–3486, 2018.
[8] Schmidhuber, J., Deep learning in neural networks: an overview. Neural Networks, 61, pp. 85–117, 2015.
[9] Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y. & Wang, Y, Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), pp. 818, 2017.
[10] Lawrence, S., Giles, C.L., Tsoi, A.C. & Back, A.D., Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 8, pp. 98–113, 1997.
[11] Ji, S., Yang, M. & Yu, K., 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis Machine Intelligence, 35, pp. 221–231, 2013.
[12] Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R. & Li, F.F., Largescale video classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1725–1732, 23–28 June 2014.
[13] Langkvist, M., Modeling Time-Series with Deep Networks, Örebro Studies in Technology, 2014.
[14] Liu, J.N., Hu, Y., He, Y., Chan, P.W. & Lai, L., Deep neural network modeling for big data weather forecasting. Information Granularity, Big Data, and Computational Intelligence, Springer, pp. 389–408, 2015.
[15] Liu, J.N., Hu, Y., You, J.J. & Chan, P.W., Deep neural network based feature representation for weather forecasting. Proceedings on the International Conference on Artificial Intelligence (ICAI). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p. 1, 2014.
[16] Kingma, D.P. & Ba, J.L., Adam: a method for stochastic optimization. International Conference on Learning Representations, pp. 1–13, 2015.
[17] Kowalski, P.A. & Kusy, M., Sensitivity analysis for probabilistic neural network structure reduction. IEEE Transactions on Neural Networks and Learning Systems, 19(5), pp. 923–937, 2018.
[18] Kowalski, P.A. & Kulczycki, P., Interval probabilistic neural network. Neural Computing and Applications, 28(4), pp. 817–834, 2017.
[19] Kowalski, P.A. & Kusy, M., Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure. Computational Intelligence, 34(3), pp. 895–916, 2018.
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Open Access
Research article

PM10 Forecasting Through Applying Convolution Neural Network Techniques

piotr a. kowalski1,2,
kasper sapała3,
wiktor warchałowski4
1
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Poland
2
Systems Research Institute, Polish Academy of Sciences, Poland
3
Kasper Sapała
4
Airly sp. z o.o, Poland
International Journal of Environmental Impacts
|
Volume 3, Issue 1, 2020
|
Pages 31-43
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

The World Health Organization (WHO) estimates that air pollution kills around 6.5 million people around the world every year. The European Environment Agency, in turn, points out that about 50,000 people die annually in Poland due to this. PM10 pollution arises in the form of smog (smoke and fog) and is an unnatural phenomenon created by adverse weather conditions and human activity. The aim of this article is to assess the possibilities of tasking modern neural networks to predict PM10 air pollution levels in the following hours of the subsequent day. In evaluating the prediction task, several types of error are considered, and machine learning algorithms and structures are utilized as learning models. Of note, the algorithm selected for stochastic optimization is a form of convolutional neural networking and deep learning neural networking that is used in machine learning when considering Big Data issues. The obtained results were then analysed and compared with other methods of prediction. As a result of this research, the proposed convergent neural network could be used effectively as a tool for calculating detailed air quality forecasts for the subsequent 24-h period.

Keywords: Air pollution prediction (forecasting), Big data, Convolutional neural networks, Machine learning, Regression task, Neural network, Particulate matters

1. Introduction

2. Investigated Models

3. Learning Procedures and Data Set

4. Empirical Study

5. Conclusions

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

This work was supported by The National Centre for Research and Development (project no. POIR.01.01.01-00-0049/17 NCBiR).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Kowalski, P.A. & Warchałowski, W., The comparison of linear models for PM10 and PM2.5 forecasting, WIT Transaction on Ecology and the Environment, 230, WIT Press: Southampton and Boston, pp. 177–188, 2018.
[2] Domańska, D. & Wojtylak, M., Application of fuzzy time series models for forecasting pollution concentrations. Expert Systems with Applications, 39(9), pp. 7673–7679, 2012.
[3] Chakraborty, K., Mehrotra, K., Mohan, C.K. & Ranka, S., Forecasting the behavior of multivariate time series using neural networks. Neural Networks, 5(6), pp. 961–970, 1992.
[4] Faruk, D.O., A hybrid neural network and arima model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), pp. 586–594, 2010.
[5] Perez, P., Menares, C. & Ramirez, C., Forecasting in the Most Polluted City in South America, WIT Transaction on Ecology and the Environment, 230, WIT Press: Southampton and Boston, pp. 199–204, 2018.
[6] Grover, A., Kapoor, A. & Horvitz, E., A deep hybrid model for weather forecasting. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 379–386, 2015.
[7] Xie, J., Wang, X., Liu, Y. & Bai, Y., Autoencoder-based deep belief regression network for air particulate matter concentration forecasting. Journal of Intelligent & Fuzzy Systems, 34(6), pp. 3475–3486, 2018.
[8] Schmidhuber, J., Deep learning in neural networks: an overview. Neural Networks, 61, pp. 85–117, 2015.
[9] Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y. & Wang, Y, Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17(4), pp. 818, 2017.
[10] Lawrence, S., Giles, C.L., Tsoi, A.C. & Back, A.D., Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 8, pp. 98–113, 1997.
[11] Ji, S., Yang, M. & Yu, K., 3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis Machine Intelligence, 35, pp. 221–231, 2013.
[12] Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R. & Li, F.F., Largescale video classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 1725–1732, 23–28 June 2014.
[13] Langkvist, M., Modeling Time-Series with Deep Networks, Örebro Studies in Technology, 2014.
[14] Liu, J.N., Hu, Y., He, Y., Chan, P.W. & Lai, L., Deep neural network modeling for big data weather forecasting. Information Granularity, Big Data, and Computational Intelligence, Springer, pp. 389–408, 2015.
[15] Liu, J.N., Hu, Y., You, J.J. & Chan, P.W., Deep neural network based feature representation for weather forecasting. Proceedings on the International Conference on Artificial Intelligence (ICAI). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p. 1, 2014.
[16] Kingma, D.P. & Ba, J.L., Adam: a method for stochastic optimization. International Conference on Learning Representations, pp. 1–13, 2015.
[17] Kowalski, P.A. & Kusy, M., Sensitivity analysis for probabilistic neural network structure reduction. IEEE Transactions on Neural Networks and Learning Systems, 19(5), pp. 923–937, 2018.
[18] Kowalski, P.A. & Kulczycki, P., Interval probabilistic neural network. Neural Computing and Applications, 28(4), pp. 817–834, 2017.
[19] Kowalski, P.A. & Kusy, M., Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure. Computational Intelligence, 34(3), pp. 895–916, 2018.
Cite this:
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Kowalski, P. A., Sapała, K., & Warchałowski, W. (2020). PM10 Forecasting Through Applying Convolution Neural Network Techniques. Int. J. Environ. Impacts., 3(1), 31-43. https://doi.org/10.2495/EI-V3-N1-31-43
P. A. Kowalski, K. Sapała, and W. Warchałowski, "PM10 Forecasting Through Applying Convolution Neural Network Techniques," Int. J. Environ. Impacts., vol. 3, no. 1, pp. 31-43, 2020. https://doi.org/10.2495/EI-V3-N1-31-43
@research-article{Kowalski2020PM10FT,
title={PM10 Forecasting Through Applying Convolution Neural Network Techniques},
author={Piotr A. Kowalski and Kasper SapałA and Wiktor WarchałOwski},
journal={International Journal of Environmental Impacts},
year={2020},
page={31-43},
doi={https://doi.org/10.2495/EI-V3-N1-31-43}
}
Piotr A. Kowalski, et al. "PM10 Forecasting Through Applying Convolution Neural Network Techniques." International Journal of Environmental Impacts, v 3, pp 31-43. doi: https://doi.org/10.2495/EI-V3-N1-31-43
Piotr A. Kowalski, Kasper SapałA and Wiktor WarchałOwski. "PM10 Forecasting Through Applying Convolution Neural Network Techniques." International Journal of Environmental Impacts, 3, (2020): 31-43. doi: https://doi.org/10.2495/EI-V3-N1-31-43
Kowalski P. A.， Sapała K.， Warchałowski W.. PM10 Forecasting Through Applying Convolution Neural Network Techniques[J]. International Journal of Environmental Impacts， 2020， 3(1)： 31-43. https://doi.org/10.2495/EI-V3-N1-31-43