Carbon Monoxide Prediction in the Atmosphere of Tehran Using Developed Support Vector Machine

Document Type: Original Research Paper

Authors

1 Water Research Institute, Ministry of Energy, P.O. Box 16765-313, Tehran, Iran The Institute for Energy and Hydro Technology, P.O. Box 14845-131Tehran, Iran

2 Department of Civil Engineering, Arak University, P.O. Box 38156-879, Arak, Iran

3 Department of Civil Engineering, Azad University South Tehran Branch, P.O. Box 15847-43311, Tehran, Iran

Abstract

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.

Keywords


Anguita, D., Boni, A. and Ridella, S. (1999). Learning algorithm for nonlinear support vector machines suited for digital VLSI. Electron. Lett., 35(16); 1349-1350.

Azeez, O., Pradhan, B. and Shafri, H. (2018). Vehicular CO emission prediction using support vector regression model and GIS. Sustainability-Basel., 10(10); 1-18.

Bayat, R. (2005). Source Apportionment of Tehran's Air Pollution. M. Sc thesis. Department of Civil and Environmental Engineering, Sharif University of Technology, Tehran, Iran,

Bray, M. and Han, D. (2004). Identification of support vector machines for runoff modelling. J. Hydroinform., 6(4); 265-280.

Brown, M., Gunn, S. R. and Lewis, H. G. (1999). Support vector machines for optimal classification and spectral unmixing. Ecol. Model., 120(2-3); 167-179.

Chen, S. T. and Yu, P. S. (2007). Real-time probabilistic forecasting of flood stages. J. Hydrol., 340(1-2); 63-77.

Chen, S., Hong, X., Harris, C. J. and Sharkey, P. M. (2004). Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE T. Syst. Man. Cy. B., 34(2); 898-911.

Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge university press.

Dehghani, M., Saghafian, B., Nasiri Saleh, F., Farokhnia, A. and Noori, R. (2014). Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte‐Carlo simulation. Int. J. Climatol., 34(4); 1169-1180.

Dibike, Y. B., Velickov, S., Solomatine, D. and Abbott, M. B. (2001). Model induction with support vector machines: introduction and applications. J. Comput. Civil Eng., 15(3); 208-216.

Eksioglu, B., Demirer, R. and Capar, I. (2005). Subset selection in multiple linear regression: a new mathematical programming approach. Comput. Ind. Eng., 49(1); 155-167.

Elangasinghe, M., Dirks, K., Singhal, N., Costello, S., Longley, I. and Salmond, J. (2014). A simple semi-empirical technique for apportioning the impact of roadways on air quality in an urban neighbourhood. Atmos. Environ., 83; 99-108.

Ghaemi, Z., Alimohammadi, A. and Farnaghi, M. (2018). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran. Environ. Monit. Assess., 190(300); 1-17.

Gokhale, S. and Pandian, S. (2007). A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection. Atmos. Environ., 41(36); 7940-7950.

Han, D., Chan, L. and Zhu, N. (2007). Flood forecasting using support vector machines. J. Hydroinform., 9(4); 267-276.

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. and Scholkopf, B. (1998). Support vector machines. IEEE Intell. Sys., 13(4); 18-28.

Hrust, L., Klaić, Z. B., Križan, J., Antonić, O. and Hercog, P. (2009). Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos. Environ., 43(35); 5588-5596.

Hsu, C. W., Chang, C. C. and Lin, C. J. (2003). A practical guide to support vector classification. Tech. repurt. Department of Computer Science, National Taiwan University.

Jalili Ghazi Zade, M. and Noori, R. (2008). Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. Int. J. Environ. Res., 2(1); 13-22.

Jorquera, H. (2002). Air quality at Santiago, Chile: a box modeling approach—I. Carbon monoxide, nitrogen oxides and sulfur dioxide. Atmos. Environ., 36(2); 315-330.

Kecman, V. (2005). Support vector machines–an introduction, in “Support Vector Machines: Theory and Applications (pp. 1-47)”. New York: Springer.

Keerthi, S. S. and Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput., 15(7); 1667-1689.

Khan, J. A., Van Aelst, S. and Zamar, R. H. (2007). Building a robust linear model with forward selection and stepwise procedures. Comput. Stat. Data An., 52(1); 239-248.

Li, X., Lord, D., Zhang, Y. and Xie, Y. (2008). Predicting motor vehicle crashes using support vector machine models. Accident Anal. Prev., 40(4); 1611-1618.

Liong, S. Y. and Sivapragasam, C. (2002). Flood stage forecasting with support vector machines. J. Am. Water Resour. As., 38(1); 173-186.

Liu, H., Yao, X., Zhang, R., Liu, M., Hu, Z. and Fan, B. (2006). The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine. Chemosphere, 63(5); 722-733.

Lu, W. Z. and Wang, W. J. (2005). Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere, 59(5); 693-701.

Luna A.S., Paredes M. L. L., De Oliveira G. C. G. and Corrêa S. M. (2014). Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos. Environ., 98; 98–104.

Moazami, S., Noori, R., Amiri, B. J., Yeganeh, B., Partani, S. and Safavi, S. (2016). Reliable prediction of carbon monoxide using developed support vector machine. Atmos. Pollut. Res., 7(3); 412-418.

Murena, F., Favale, G., Vardoulakis, S. and Solazzo, E. (2009). Modelling dispersion of traffic pollution in a deep street canyon: application of CFD and operational models. Atmos. Environ., 43(14); 2303-2311.

Noori, R., Ashrafi, K. and Azhdarpour, A. (2008). Comparison of ANN and PCA based multivariate linear regression applied to predict the daily average concentration of CO: A case study of Tehran. J. Earth. Space. Phys., 34(1); 135-152.

Noori, R., Abdoli, M. A., Ghasrodashti, A. A. and Jalili Ghazizade, M. (2009a). Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad. Environ. Prog. Sustain., 28(2); 249-258.

Noori, R., Karbassi, A., Farokhnia, A. and Dehghani, M. (2009b). Predicting the longitudinal dispersion coefficient using support vector machine and adaptive neuro-fuzzy inference system techniques. Environ. Eng. Sci., 26(10); 1503-1510.

Noori, R., Hoshyaripour, G., Ashrafi, K. and Araabi, B. N. (2010a). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos. Environ., 44(4); 476-482.

Noori, R., Karbassi, A. and Sabahi, M. S. (2010b). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. J. Environ. Manage., 91(3); 767-771.

Noori, R., Karbassi, A., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M., Farokhnia, A. and Gousheh, M. G. (2011). Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J. Hydrol., 401(3-4); 177-189.

Noori, R., Yeh, H. D., Abbasi, M., Kachoosangi, F. T. and Moazami, S. (2015a). Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand. J. Hydrol., 527; 833-843.

Noori, R., Deng, Z., Kiaghadi, A. and Kachoosangi, F.T. (2015b). How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers?. J. Hydraul. Eng., 142(1); p.04015039.

Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R. and Chatterton, T. (2004). Modelling SO2 concentration at a point with statistical approaches. Environ. Modell. Softw., 19(10), 887-905.

Osowski, S. and Garanty, K. (2007). Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng. Appl. Artif. Intel., 20(6); 745-755.

Pelliccioni, A. and Tirabassi, T. (2006). Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations. Environ. Modell. Softw., 21(4); 539-546.

Perez-Roa, R., Castro, J., Jorquera, H., Perez-Correa, J. and Vesovic, V. (2006). Air-pollution modelling in an urban area: Correlating turbulent diffusion coefficients by means of an artificial neural network approach. Atmos. Environ., 40(1); 109-125.

Pérez, P., Trier, A. and Reyes, J. (2000). Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmos. Environ., 34(8); 1189-1196.

Sahin, U., Ucan, O. N., Soyhan, B. and Bayat, C. (2004). Modeling of CO distribution in Istanbul using artificial neural networks. Fresenius Environ. Bullet., 13(9); 839-845.

Salazar-Ruiz, E., Ordieres, J., Vergara, E. and Capuz-Rizo, S. F. (2008). Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). Environ. Modell. Softw., 23(8); 1056-1069.

Shakerkhatibi, M., Mohammadi, N., Zoroufchi Benis, K., Behrooz Sarand, A., Fatehifar, E. and Asl Hashemi, A. (2015). Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz. Environ. Health Eng. Manage. J., 2(3); 117-122.

Vapnik, V. (1998). Statistical learning theory. (Vol. 3) New York: Wiley.

Wang, W., Xu, Z., Lu, W. and Zhang, X. (2003). Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing, 55(3-4); 643-663.

Wang, X., Chen, S., Lowe, D. and Harris, C. J. (2006). Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing, 70(1-3); 462-474.

Wang, Z., Lu, F., Lu, Q. C., Wang, D. and Peng, Z. R. (2015). Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm. Atmos. Environ., 104; 264-272.

Yu, P. S., Chen, S. T. and Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. J. Hydrol., 328(3-4); 704-716.