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2017 | OriginalPaper | Buchkapitel

Determination of Modeling Parameters for a Low Cost Air Pollution Measurement System Using Feedforward Neural Networks

verfasst von : Nur Azie Dahari, Herman Wahid

Erschienen in: Modeling, Design and Simulation of Systems

Verlag: Springer Singapore

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Abstract

Air pollution model is commonly used to predict the pollutant level in the air for the upcoming days based on the previous data. In this paper, a new model for predicting ozone, nitrogen dioxide and sulphur dioxide will be developed using the previous data of pollutants agents such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone, particulate matter and the meteorological data includes wind speed, temperature and humidity. It is developed to improve the estimation values for a low cost setup of air pollution measurement system. The models are constructed using the Levenberg-Marquardt training algorithms in the neural network tool. Different input parameters are investigated to develop better performance model for predicting air pollution. The proposed model is capable to predict the air pollution level with high accuracy and the meteorological data are dominantly influenced the accuracy of the model.

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Asghari, M., Nematzadeh, H.: Predicting air pollution in tehran: genetic algorithm and back propagation neural network. J. Al. Data Min. 4(1), 49–54 (2016)

Fleischer, N.L., Merialdi, M., Van Donkelaar, A., Vadillo-Ortega, F., Martin, R.V., Betran, A.P.: Outdoor air pollution, preterm birth, and low birth weight: analysis of the world health organization global survey on maternal and perinatal health. Environ. Health Perspect. 122(4), 425–430 (2014)

Khader, Y., Abdelrahman, M., Abdo, N., Awad, S., Al-Sharif, M., Elbetieha, A., Malkawi, A.: Exposure to air pollution and pregnancy outcomes in the east mediterranean region: a systematic review. Int. J. Pediatr. 4(1), 1255–1271 (2016)

Zallaghi, E., Geravandi, S., Haddad, M.N., Goudarzi, G., Valipour, L., Salmanzadeh, S., Soltani, F.: Estimation of health effects attributed to nitrogen dioxide exposure using the airq model in tabriz city, Iran. J. Health Scope 4(4), 31–37 (2015)

Pawul, M., Śliwka, M.: Application of artificial neural networks for prediction of air pollution levels in environmental monitoring. J. Ecol. Eng. 17(4), 190–196 (2016)CrossRef

Lakey, P.S., Wisthaler, A., Berkemeier, T., Mikoviny, T., Pöschl, U., Shiraiwa, M.: Chemical Kinetics of Multiphase Reactions Between Ozone and Human Skin Lipids: Implications for Indoor Air Quality and Health Effects. Indoor Air (2016)

Khafaie, M.A., Yajnik, C.S., Salvi, S.S., Ojha, A.: Critical review of air pollution health effects with special concern on respiratory health. J. Air Pollut. Health 1(2), 123–136 (2016)

Demuth, H., Beale, M., Hagan, M.: Neural network toolbox: for use with MATLAB: user’s guide: version 5. MathWorks, Natick, MA (2007)

Abdul-Wahab, S.A., Al-Alawi, S.M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ. Modell Softw. 17(3), 219–228 (2002)CrossRef

10.

Sabah, A., Al-Rubiei, R., Al-Shamsi, A.: A statistical model for predicting carbon monoxide levels. Int. J. Environ. Pollut. 19, 209–224 (2003)CrossRef

11.

Chaudhuri, S., Acharya, R.: Artificial neural network model to forecast the concentration of pollutants over delhi: skill assessment of learning rules. Asian J. Water Environ. Pollut. 1, 71–81 (2012)

12.

Pauzi, H.M., Abdullah, L.: Neural network training algorithm for carbon dioxide emissions forecast: a performance comparison. In: Sulaiman, H.A., Othman, M.A., Othman, M.F.I., Rahim, Y.A., Pee, N.C. (eds.) Advanced Computer and Communication Engineering Technology. LNEE, vol. 315, pp. 717–726. Springer, Cham (2015). doi:10.1007/978-3-319-07674-4_67

13.

Li, W., Wu, X., Jiao, W., Qi, G., Liu, Y.: Modeling of dust removal in rotating packed bed using artificial neural networks (ANN). Appl. Therm. Eng. 112, 208–213 (2017)CrossRef

14.

Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J.: Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107, 118–128 (2015)CrossRef

15.

Swingler, K.: Applying Neural Networks: A Practical Guide. Academic Press, London (1996)

16.

Saha, D.: Prediction of mass transfer coefficient in rotating bed contactor (Higee) Using artificial neural network. Heat Mass Transfer. 45, 451–457 (2009)CrossRef

17.

Lashkarbolooki, M., Vaferi, B., Mowla, D.: Using artificial neural network to predict the pressure drop in a rotating packed bed. Sep. Sci. Technol. 47, 2450–2459 (2012)

18.

Memarianfard, M., Hatami, A.M.: Artificial neural network forecast application for fine particulate matter concentration using meteorological data. Global J. Environ. Sci. Manag. 3(3), 333–340 (2017)

19.

Wahid, H., Ha, Q.P., Nguyen-Duc, H.: A Metamodel for background ozone level using radial basis function neural networks. In: Control Automation Robotics and Vision (ICARCV), pp. 958–963 (2010)

Titel: Determination of Modeling Parameters for a Low Cost Air Pollution Measurement System Using Feedforward Neural Networks
verfasst von: Nur Azie Dahari
Herman Wahid
Verlag: Springer Singapore
Buch: Modeling, Design and Simulation of Systems
Print ISBN: 978-981-10-6462-3

Electronic ISBN: 978-981-10-6463-0

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-6463-0_58

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