Abstract
Every year, cities all over the world face the problem of the air pollution. In particular seasons, such as the winter, the levels of bad particles coming from the industrial and domestic heating systems increase the risk of pulmonary diseases. Thus, for both the city majors and citizens, it is important to understand and predict the air pollution levels in advance to safe guard the health. Modern forecasting systems are able to alert the population in advance only about the general weather condition, while the air quality information are almost not considered at all. The reasons are manifold and they mostly depend by the difficult that the modern systems have to generalize the problem and correct elaborate the data coming from the sensors. In this paper we address the problem of forecasting the bands of the different air pollutants according to the Air Quality Index in the Apulia region. Using two different Recurrent Neural Network models, we have performed two tests to prove that it is possible to predict the level of the pollutants in a specific area by using the data coming from the surrounding area. By using this approach on both the weather and air stations on the territory it is possible to have alerts many days ahead on the pollution levels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
http://dati.arpa.puglia.it/dataset/meteo-nrt: weather dataset, last visited 23/01/2019.
- 2.
http://dati.arpa.puglia.it/dataset/aria-nrt: air dataset, last visited 23/01/2019.
References
Anderson, J.O., Thundiyil, J.G., Stolbach, A.: Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8(2), 166–175 (2012)
Bevilacqua, V., Cassano, F., Mininno, E., Iacca, G.: Optimizing feed-forward neural network topology by multi-objective evolutionary algorithms: a comparative study on biomedical datasets. In: Italian Workshop on Artificial Life and Evolutionary Computation, pp. 53–64. Springer, Cham (2015)
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmainen, M.: Evolving the neural network model for forecasting air pollution time series. Eng. Appl. Artif. Intell. 17(2), 159–167 (2004)
Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J.: Artificial neural networks forecasting of PM\(_{2.5}\) pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107, 118–128 (2015)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)
Huang, C.-J., Kuo, P.-H.: A deep CNN-LSTM model for particulate matter (PM\(_{2.5}\)) forecasting in smart cities. Sensors 18(7), 2220 (2018)
Stevens, M., D’Hondt, E.: Crowdsourcing of pollution data using smartphones. In: Workshop on Ubiquitous Crowdsourcing (2010)
Adams, M.D., Kanaroglou, P.S.: Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models. J. Environ. Manage. 168, 133–141 (2016)
Sivacoumar, R., Bhanarkar, A., Goyal, S., Gadkari, S., Aggarwal, A.: Air pollution modeling for an industrial complex and model performance evaluation. Environ. Pollut. 111(3), 471–477 (2001)
To, T., Shen, S., Atenafu, E.G., Guan, J., McLimont, S., Stocks, B., Licskai, C.: The air quality health index and asthma morbidity: a population-based study. Environ. Health Perspect. 121(1), 46–52 (2012)
Cheng, W.-L., Chen, Y.-S., Zhang, J., Lyons, T., Pai, J.-L., Chang, S.-H.: Comparison of the revised air quality index with the PSI and AQI indices. Sci. Total Environ. 382(2–3), 191–198 (2007)
Sowlat, M.H., Gharibi, H., Yunesian, M., Mahmoudi, M.T., Lotfi, S.: A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmos. Environ. 45(12), 2050–2059 (2011)
Caivano, D., Cassano, F., Fogli, D., Lanzilotti, R., Piccinno, A.: We@ home: a gamified application for collaboratively managing a smart home. In: International Symposium on Ambient Intelligence, pp. 79–86, Springer, Cham (2017)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
World Health Organization: Air quality guidelines for Europe (2000)
Acknowledgment
The present study was developed and granted in the framework of the project: “SeVaRA” (European Community, Minister of the Economic Development, Apulia Region, BURP n. 1883 of the 24/10/2018, Id:2NQR592).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cassano, F., Casale, A., Regina, P., Spadafina, L., Sekulic, P. (2020). A Recurrent Neural Network Approach to Improve the Air Quality Index Prediction. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds) Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence. ISAmI 2019. Advances in Intelligent Systems and Computing, vol 1006 . Springer, Cham. https://doi.org/10.1007/978-3-030-24097-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-24097-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24096-7
Online ISBN: 978-3-030-24097-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)