Long-term time-series pollution forecast using statistical and deep learning methods

Pritthijit Nath; Pratik Saha; Asif Iqbal Middya; Sarbani Roy

doi:10.1007/s00521-021-05901-2

journal article Apr 03, 2021

Long-term time-series pollution forecast using statistical and deep learning methods

Pritthijit Nath Pratik Saha Asif Iqbal Middya Sarbani Roy

Neural Computing and Applications Vol. 33 No. 19 pp. 12551-12570 · Springer Science and Business Media LLC

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References

65

[1]

Mahajan S, Chen LJ, Tsai TC (2017) An empirical study of PM2.5 forecasting using neural network. https://doi.org/10.1109/UIC-ATC.2017.8397443 10.1109/uic-atc.2017.8397443

[2]

Xiang X (2019) Forecasting air pollution PM2.5 in beijing using weather data and multiple kernel learning. J Forecast. https://doi.org/10.1002/for.2599 10.1002/for.2599

[3]

Xie J (2017) Deep neural network for PM2.5 pollution forecasting based on manifold learning. In: 2017 international conference on sensing, diagnostics, prognostics, and control (SDPC), pp 236–240 10.1109/sdpc.2017.52

[4]

Luo C, Yang H, Huang L, Mahajan S, Chen L (2018) A fast PM2.5 forecast approach based on time-series data analysis, regression and regularization. In: 2018 conference on technologies and applications of artificial intelligence (TAAI), pp 78–81 10.1109/taai.2018.00026

[5]

Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128. https://doi.org/10.1016/j.atmosenv.2015.02.030. http://www.sciencedirect.com/science/article/pii/S1352231015001491 10.1016/j.atmosenv.2015.02.030

[6]

Haiming Z, Xiaoxiao S (2013) Study on prediction of atmospheric PM2.5 based on RBF neural network. In: 2013 4th international conference on digital manufacturing automation, pp 1287–1289 10.1109/icdma.2013.306

[7]

Yan L, Wu Y, Yan L, Zhou M (2018) Encoder–decoder model for forecast of PM2.5 concentration per hour. In: 2018 1st international cognitive cities conference (IC3), pp 45–50 10.1109/ic3.2018.00020

[8]

Cortina-Januchs MG, Quintanilla-Dominguez J, Vega-Corona A, Andina D (2015) Development of a model for forecasting of PM10 concentrations in Salamanca, Mexico. Atmos Pollut Res 6(4):626–634. https://doi.org/10.5094/APR.2015.071. http://www.sciencedirect.com/science/article/pii/S1309104215301951 10.5094/apr.2015.071

[9]

Al-kasassbeh M, Sheta A, Faris H, Turabieh H (2013) Prediction of PM10 and tsp air pollution parameters using artificial neural network autoregressive, external input models: a case study in salt, jordan. Middle-East J Sci Res 14:999–1009. https://doi.org/10.5829/idosi.mejsr.2013.14.7.2171 10.5829/idosi.mejsr.2013.14.7.2171

[10]

Lam LH, Mok KM (2007) Prediction of ambient pm10 concentration with artificial neural network. In: Computational methods in engineering and science. Springer, Berlin, Heidelberg, p 276 10.1007/978-3-540-48260-4_122

[11]

Das M, Maiti SK, Mukhopadhyay U (2006) Distribution of PM2.5 and PM10-2.5 in PM10 fraction in ambient air due to vehicular pollution in Kolkata megacity. Environ Monit Assess 122(1–3):111–123 10.1007/s10661-005-9168-3

[12]

Jiao K, Xu M, Liu M (2018) Health status and air pollution related socioeconomic concerns in urban china. Int J Equ Health 17(1):1–11 10.1186/s12939-017-0710-z

[13]

Ong BT, Sugiura K, Zettsu K (2016) Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Comput Appl 27:1553–1566. https://doi.org/10.1007/s00521-015-1955-3 10.1007/s00521-015-1955-3

[14]

Bashir Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sensors J 16(8):2598–2606 10.1109/jsen.2016.2514378

[15]

Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

Qing Tao, Fang Liu, Yong Li et al.

IEEE Access 2019 10.1109/access.2019.2921578

[16]

Liang X, Zou T, Guo B, Li S, Zhang H, Zhang S, Huang H, Chen S (2015) Assessing Beijing’s PM2.5 pollution: severity, weather impact, apec and winter heating. Proc R Soc A: Math, Phys Eng Sci 471:257. https://doi.org/10.1098/rspa.2015.0257 10.1098/rspa.2015.0257

[17]

Mlakar P (1997) Determination of features for air pollution forecasting models. In: Proceedings intelligent information systems, IIS’97, pp 350–354 10.1109/iis.1997.645291

[18]

Li T, Hua M, Wu X (2020) A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5). IEEE Access 8:26933–26940 10.1109/access.2020.2971348

[19]

Wang W, Guo Y (2009) Air pollution PM2.5 data analysis in los angeles long beach with seasonal arima model. In: 2009 international conference on energy and environment technology, vol 3, pp 7–10 10.1109/iceet.2009.468

[20]

Bai L, Wang J, Ma X, Lu H (2018) Air pollution forecasts: an overview. Int J Environ Res Public Health 15(4):780 10.3390/ijerph15040780

[21]

Kurt A, Gulbagci B, Karaca F, Alagha O (2008) An online air pollution forecasting system using neural networks. Environ Int 34(5):592–598 10.1016/j.envint.2007.12.020

[22]

Xu X (2020) Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning. J Forecast 39(2):117–125 10.1002/for.2599

[23]

Norazian MN, Shukri YA, Azam RN et al (2008) Estimation of missing values in air pollution data using single imputation techniques. ScienceAsia 34(3):341–345 10.2306/scienceasia1513-1874.2008.34.341

[24]

Bandyopadhyay K (1644) Banned vehicles found plying in kolkata in november. Times News Network. http://timesofindia.indiatimes.com/articleshow/73062554.cms

[25]

MacNee W, Donaldson K (2003) Mechanism of lung injury caused by PM10 and ultrafine particles with special reference to COPD. Eur Respir J 21(40 suppl):47s–51s. https://doi.org/10.1183/09031936.03.00403203. https://erj.ersjournals.com/content/21/40_suppl/47s 10.1183/09031936.03.00403203

[26]

Ministry of Environment, Forest and Climate Change, Govt. of India: Central Pollution Control Board. http://www.cpcb.nic.in/. Accessed 15 Aug 2020

[27]

Kissock JK University of Dayton Average Daily Temperature Archive. http://academic.udayton.edu/kissock/http/Weather/. Accessed 15 Aug 2020

[28]

The Weather Company (IBM): Weather Underground. https://www.wunderground.com/. Accessed 15 Aug 2020

[29]

US Department of State: Air Now International US Embassies and Consulates. https://www.airnow.gov/international/us-embassies-and-consulates/. Accessed 15 Aug 2020

[30]

Buck SF (1960) A method of estimation of missing values in multivariate data suitable for use with an electronic computer. J R Stat Soc, Ser B (Methodol) 22(2):302–306. http://www.jstor.org/stable/2984099 10.1111/j.2517-6161.1960.tb00375.x

[31]

Hodrick RJ, Prescott EC (1997) Postwar U.S. business cycles: an empirical investigation. J Money, Credit Banking 29(1):1–16. http://www.jstor.org/stable/2953682 10.2307/2953682

[32]

Box GEP, Jenkins G (1990) Time series analysis, Forecasting and control. Holden-Day Inc, USA

[33]

Fuller WA (1976) Introduction to statistical time series. Wiley, New York

[34]

Manuca R, Savit R (1996) Stationarity and nonstationarity in time series analysis. Phys. D: Nonlinear Phenom 99(2–3):134–161 10.1016/s0167-2789(96)00139-x

[35]

Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6(3):324–342. https://doi.org/10.1287/mnsc.6.3.324 10.1287/mnsc.6.3.324

[36]

Walker GT (1931) On periodicity in series of related terms. Proc R Soc Lond, Ser A, Contain Pap Math Phys Character 131(818):518–532

[37]

Forecasting at Scale

Sean J. Taylor, Benjamin Letham

The American Statistician 2018 10.1080/00031305.2017.1380080

[38]

Long Short-Term Memory

Sepp Hochreiter, Jürgen Schmidhuber

Neural Computation 1997 10.1162/neco.1997.9.8.1735

[39]

Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233–243. https://doi.org/10.1002/aic.690370209 10.1002/aic.690370209

[40]

Bidirectional recurrent neural networks

M. Schuster, K.K. Paliwal

IEEE Transactions on Signal Processing 1997 10.1109/78.650093

[41]

Gradient-based learning applied to document recognition

Y. Lecun, L. Bottou, Y. Bengio et al.

Proceedings of the IEEE 1998 10.1109/5.726791

[42]

Parallel Distributed Processing

David E. Rumelhart, James L. McClelland

10.7551/mitpress/5236.001.0001

[43]

Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th international conference on neural information processing systems, vol 1. MIT Press, pp 802–810

[44]

Cortina-Januchs MG, Quintanilla-Dominguez J, Vega-Corona A, Andina D (2015) Development of a model for forecasting of PM10 concentrations in Salamanca. Mexico. Atmos Pollut Res 6(4):626–634 10.5094/apr.2015.071

[45]

Middya AI, Roy S, Dutta J, Das R (2020) Jusense: a unified framework for participatory-based urban sensing system. Mob Netw Appl 25:1249–1274 10.1007/s11036-020-01539-x

[46]

Dutta J, Chowdhury C, Roy S, Middya A, Gazi F (2017) Towards smart city: sensing air quality in city based on opportunistic crowd-sensing. In: Proceedings of the 18th international conference on distributed computing and networking. Association for Computing Machinery 10.1145/3007748.3018286

[47]

Wang X, Smith-Miles K, Hyndman R (2009) Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series. Neurocomputing 72(10–12):2581–2594 10.1016/j.neucom.2008.10.017

[48]

Armstrong JS (2001) Principles of forecasting: a handbook for researchers and practitioners, vol 30. Springer 10.1007/978-0-306-47630-3

[49]

Meade N (2000) Evidence for the selection of forecasting methods. J Forecast 19(6):515–535 10.1002/1099-131x(200011)19:6<515::aid-for754>3.0.co;2-7

[50]

Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley

Showing 50 of 65 references

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Published: Apr 03, 2021
Vol/Issue: 33(19)
Pages: 12551-12570
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Pritthijit Nath, Pratik Saha, Asif Iqbal Middya, et al. (2021). Long-term time-series pollution forecast using statistical and deep learning methods. Neural Computing and Applications, 33(19), 12551-12570. https://doi.org/10.1007/s00521-021-05901-2

Long-term time-series pollution forecast using statistical and deep learning methods

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