A wavelet-ANN-based framework for estimating air pollutant concentrations using remotely sensed data in Tehran metropolitan area

Subject Areas : Regional Planning

Ali Shamsoddini ¹ (Assistant Professor, Department of Remote Sensing,Tarbiat Modares University, Tehran, Iran.)
Mohammad Reza Aboodi ² (Phd Student of Remote Sensing,Tarbiat Modares University, Tehran, Iran.)

Received: 2020-08-14 Accepted : 2021-04-12 Published : 2022-10-23

Keywords: Modeling, air pollution, Wavelet transform, Random Forest, Artificial neural network Multilayer perceptron,

Abstract :

In developing countries, most major cities are increasingly exposed to air pollution as a factor affecting the quality of life and public health of the community. High population density in Tehran causes this metropolitan area to be one of the most important region in Iran. Polluting industry and the use of polluting transportation are two of the main sources of air pollutant in Tehran and have turned this city to the most polluted metropolitan area in Iran. Consequently, the need for the air pollution reduction is too necessary in this area. The air pollutant concentration predictions can improve decision making for appropriate solutions to reduce air pollution. Since more precise methods are required to predict air pollutants for better management of this problem, using hybrid methods can be an important step in modeling different pollutants. This study examined the performance of the random forest feature selection and wavelet transformation methods when they combine with the multiple-linear regression and multilayer perceptron artificial neural network to achieve an efficient model to estimate several pollutants including carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5 in Tehran metropolitan area. For these purpose four groups of remotely sensed-derived and spatial data including spatial data, meteorological data, traffic information, and the air pollutant concentrations in the days before the prediction day were applied as the input data of the models. Results showed that the modeling of all pollutants by the multilayer perceptron neural network along with the wavelet transform method provides higher accuracy than the other models. Furthermore, the estimation accuracy of the carbon monoxide pollutant (with error of estimation=19.8% ) was lower than the other pollutants while PM2.5 (with error of estimation=17.0%) was estimated with higher accuracy compared to that derived for other pollutants. Moreover, it was shown that the pollutant concentrations for the days before the day for that the estimation is implemented are the most important attributes, according to the random forest feature selection method.

References:

• Ahadi, M., Sajadi, Zh., Yarigholi, V., (2019): Analysis and evaluation of livability indicators in urban areas Case study: 34 districts of Zanjan. Journal of Regional planning, 9(34), 131-148(In Persian).


• Ahadnejad, P., Khaledi, Sh., Ahmadi, M., (2020): Investigating the Long-term effect of dust on Health in order to prevent Its Impacts in Future Planning Case Study: Khuzestan Province. Journal of Regional planning, 10(39), 33-36(In Persian).


• Akbari, M.,& Samadzadegan, F., (2015): Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method. Int. J. Environ. Sci. Technol, 12, 3551–3562.


• Antanasijević, V. Pocajt, D. Povrenović, M. Ristić, A. Perić-Grujić., (2013): PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization , Science of the Total Environment , Vol. 443, pp. 511–519.


• Arhami, M., Kamali, N., Rajabi, M., (2013): Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. Environ Sci Pollut Res, 20, 4777–4789.


• Barrero, M.A., Grimalt, J.O., Canton, L., (2006): Prediction of daily ozone concentration maxima in the urban atmosphere. Chemom. Intell. Lab. Sys. 80, 67-76.


• Dunea, D., Pohoata, A., Iordache, S., (2015): Using wavelet–feedforward neural networks to improve air pollution forecasting in urban environments. Environ Monit Assess, 187(7),1-16.


• Durao, M., Mendes, T., Pereira, M., (2016): Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmospheric Pollution Research, 7, 961-970.


• Esmailnejad, M., Eskandari Sani, M., Borzaman, S., (2015): Evaluation and Zoning of Urban air Pollution in Tabriz. Journal of Regional planning, 5(19), 173-186(In Persian)


• 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. Atmospheric Environment, 107, 118-128.


• Fernando, H. J. S., Mammarella, M. C., Grandoni, C., Fedele, P., Di Marco, R., Dimitrova, R., Hyde, P., (2012): Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environ. Pollut. 163, 62-67.


• Ghafouri Kesbi, F., Rahimi Mianji, G., Honarvar, M., Nejati Javaremi, A., (2016): Tuning and Application of Random Forest Algorithm in Genomic Evaluation. Research on Animal Production, 7(13), spring and Summer(In Persian).


• Grivas, G., & Chaloulakou, A., (2006): Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40, 1216 – 1229.


• Karatzas, K. D.,& Kaltsatos, S., (2007): Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simulation Modelling Practice and Theory, 15, 1310–1319.


• Lee, S., Ho, CH., Choi, YS, (2011): High-PM10 concentration episodes in Seoul, Korea: background sources and related meteorological conditions. Atmos Environ, 45(39), 7240–7247.


• Mohammadi, N., Khatibi, KH., Shaker khatibi, M,. Fatehi far,E., (2016): Predicting the concentration of gaseous pollutants in the air of Tabriz using a neural network. Civil and Environmental Engineering, 83(46) (In Persian).


• Moustris, K. P., Larissi, I. K., Nastos, P. T., Koukouletsos, K. V., Paliatsos, A. G., (2013): Development and Application of Artificial Neural Network Modeling in Forecasting PM10 Levels in a Mediterranean City. Water Air Soil Pollut, 224(8), 1634-1642.


• Noorani, V., Karimzadeh, H., Najafi, H., Hosseini, A., (2019): Predicting the concentration of NO2 and SO2 pollutants in the air of Tabriz using artificial neural network and adaptive neural-fuzzy inference system and comparing the obtained results. International Conference on civil engineering ,architectureand urban planning(In Persian).


• Osowski, S.,& louGaranty, K., (2007): Forecasting of the daily meteor ological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence, 20, 745 – 755.


• Perez, P., & Trier, A., (2011): Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile. Atmos. Environ., 35, 1783-1789.


• Sadr Mousavi,M.S., & Rahimi,A., (2010): Comparison of Multilayer Perceptron Neural Networks with multiple regression to predict the concentration of ozone in Tabriz, Natural Geography Research, Vol. 71, pp. 65-72(In Persian).


• Shamsoddini, A., Raval, S., Taplin, R., (2014): Spectroscopic analysis of soil metal contamination atound a derelictmine site in the blue mountains, australia”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-7, 2014 ISPRS Technical Commission VII Symposium, 29 September – 2 October 2014, Istanbul, Turkey.


• Shamsoddini, A., Trinder, J. C., Turner, R., (2015): Paired-data fusion for improved estimation of pine plantation structure. International Journal of Remote Sensing, 36, 1995-2009.


• Shamsoddini, A., Aboodi, M. R., Karami, J., (2017): Tehran air pollutants prediction based on random forest feature selection method. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W4, 2017 Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–10 October 2017, Tehran, Iran.


• Sharma, M., Aggrawal, S., Bose, P., (2002): Meteorology – base forecasting of air quality index using neural network. International conference neural network, Singapoure, 374-378.


• Siwek, K., & Osowaski, S. S., (2012): Improving the accuracy of predict ion of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Engineering Applications of Artificial Intelligence, 25, 1246–1258.


• Tavakoli, M., & Esmaeili, A., (2014): Performance of ANN and fuzzy neural network adaptive for estimating of the concentration of suspended particles in the air of Tehran. Journal of Environmental Science and Engineering, 2, 75-84(In Persian).


• Wang, P., Liu, Y., Qin, Z., Zhang, G., (2015): A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Science of the Total Environment, 505, 1202–1212.


• Zhang, H., Zhang, W., Palazoglu, A., Sun, W., (2012): Prediction of ozone levels using a Hidden Markov Model HMM with Gamma distribution. Atmos Environ, 62, 64–73.