Abstract
Growing economy of a country is actually leading to harm for its atmosphere. Due to increase in the number of vehicles and industrial development in or around a city, air pollution has also escalated, which has started affecting health of the citizens. Therefore, the level of air pollution of a city needs to be monitored regularly in real-time to maintain the air quality. The state of the air of a city is described by a dimensionless value known as air quality index (AQI). In order to find a pattern from the time-series data, several techniques have been reported in literature such as linear regression, support vector machine, neural network. In this paper, we propose a method based on deep neural network architecture namely recurrent neural network (RNN) and memory cell called as long-short-term-memory (LSTM) for estimation of AQI of a city on future dates using the seasonal trends of the recorded time-series data. Simulation results confirm that the proposed method outperforms in terms of both root mean square error and Min/Max aggregation of AQI values compared to a state-of-the-art technique of AQI estimation.
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References
The World Health Report 2000: Health Systems - Improving Performance (2000). http://www.who.int/whr/2000/en/
8 People Die in Delhi Every Day due to Pollution (2018). http://www.thehindu.com/news/cities/Delhi/8-people-die-in-Delhi-every-day-due-to-pollution-SC/article17205973.ece
CPCB: Average Report Criteria (2018). http://www.cpcb.gov.in/caaqm/Auth/frmViewReportNew.aspx
Pollution Index by City 2018 (2018). https://www.numbeo.com/pollution/rankings.jsp
RMSPropOptimizer (2018). https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer
Chen, T.-M., Kuschner, W.G., Gokhale, J., Shofer, S.: Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am. J. Med. Sci. 333(4), 249–256 (2007)
Chen, B., Kan, H.: Air pollution and population health: a global challenge. Environ. Health Prev. Med. 13(2), 94–101 (2008)
Ganesh, S.S., Modali, S.H., Palreddy, S.R., Arulmozhivarman, P.: Forecasting air quality index using regression models: a case study on Delhi and Houston. In: Proceedings of the ICEI, pp. 248–254 (2017)
Ganesh, S.S., Reddy, N.B., Arulmozhivarman, P.: Forecasting air quality index based on Mamdani fuzzy inference system. In: Proceedings of the ICEI, pp. 338–341 (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org
Ivanov, V., Georgieva, I.: Air quality index evaluations for Sofia City. In: Proceedings of the IEEE EUROCON, pp. 920–925 (2017)
Kampa, M., Castanas, E.: Human health effects of air pollution. Environ. Pollut. 151(2), 362–367 (2008)
Kanchan, K., Goyal, P.: A review on air quality indexing system. Asian J. Atmos. Environ. 9(4), 101–113 (2015)
Kang, Z., Qu, Z.: Application of BP neural network optimized by genetic simulated annealing algorithm to prediction of air quality index in Lanzhou. In: Proceedings of the IEEE ICCIA, pp. 155–160 (2017)
Kök, İ., Şimşek, M.U., Özdemir, S.: A deep learning model for air quality prediction in smart cities. In: Proceedings of the IEEE International Conference on Big Data, pp. 1983–1990 (2017)
Kumar, A., Goyal, P.: Forecasting of air quality in Delhi using principal component regression technique. Atmos. Pollut. Res. 2(4), 436–444 (2011)
Kyrkilis, G., Chaloulakou, A., Kassomenos, P.A.: Development of an aggregate air quality index for an urban Mediterranean agglomeration: relation to potential health effects. Environ. Int. 33(5), 670–676 (2007)
Petr, H., Olej, V.: Predicting common air quality index - the case of Czech Microregions. Aerosol Air Qual. Res. 15, 544–555 (2015)
Puri, P., Kumar, S., Kathuria, S., Ramesh, V.: Effects of air pollution on the skin: a review. Indian J. Derm.Logy, Venereol., Leprol. 3(4), 415 (2017)
Saxena, A., Shekhawat, S.: Ambient air quality classification by Grey Wolf optimizer based support vector machine. J. Environ. Public Health 2017(3131083), 12 (2017)
Song, L.: Impact analysis of air pollutants on the air quality index in jinan winter. In: Proceedings of the IEEE CSE-EUC, pp. 471–474 (2017)
Yang, Y., Zheng, Z., Bian, K., Song, L., Han, Z.: Real-time profiling of fine-grained air quality index distribution using UAV sensing. IEEE Internet Things J. 5(1), 186–198 (2018)
Youping, L., Ya, T., Zhongyu, F., Hong, Z., Zhengzheng, Y.: Assessment and comparison of three different air quality indices in China. Environ. Eng. Res. 23(1), 21–27 (2017)
Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: Proceedings of the IEEE, pp. 929–934 (2015)
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Sharma, A., Mitra, A., Sharma, S., Roy, S. (2018). Estimation of Air Quality Index from Seasonal Trends Using Deep Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_50
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