References
- Al-Alawi, S.M., Abdul-Wahab, S.A., Bakheit, C.S. (2008) Combining Principal Component Regression and Artificial Neural Networks for More Accurate Predictions Of Ground-Level Ozone. Environmental Modelling & Software 23, 396-403. https://doi.org/10.1016/j.envsoft.2006.08.007
- Asadollahfardi, G., Taklify, A., Ghanbari, A. (2012) Application of Artificial Neural Network to Predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering 138(4), 363-370. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000402
- Bodaghpour, S., Abrahemi, M., Javadi, S. (2011) Prediction of Air Quality in District 12 in Tehran using the ANN. The 6th National Congress of Civil Engineering, University of Semnan, Semnan, Iran.
- Charkhastani, A., Bodaghpour, S. (2008) Prediction of Air Pollution Concentration in Tehran using Neural Network. 2nd Environment Conference, University of Tehran, Tehran.
- Chattopadhay, S., Chattopadhay, G. (2012) Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis. Pure and Applied Geophyscis 169, 1891-1908. https://doi.org/10.1007/s00024-011-0437-5
- Cruz-Nunez, X., Hernandez-Solis, J.M., Ruiz-Suarez, L.G. (2003) Evaluation of Vapor Recovery System's Efficiency and Personal Exposure in Service Stations in Mexico City. Science of the Total Environment 309, 59-68. https://doi.org/10.1016/S0048-9697(03)00048-2
- Cybenko, G. (1989) Approximate by Superpositions of a Sigmoid Function. Mathematics of Control. Signals, and System 2(4), 303-314. https://doi.org/10.1007/BF02551274
- Dawson, C.W., Wibly, R.L. (2001) Hydrological Modeling using Artificial Neural Networks. Progress in Physical Geography 25(80), 81-108.
- Dean, B.J. (1985) Recent Findings On The Genetic Toxicology of Benzene, Toluene, Xylenes and Phenols. Mutation Research/Review in Genetic Toxicology 154, 153-181. https://doi.org/10.1016/0165-1110(85)90016-8
-
Grivas, G., Chaloulakou, A. (2006) Artificial Neural Network Models for Prediction of
$PM_{10}$ Hourly Concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment 40, 1216-1229. https://doi.org/10.1016/j.atmosenv.2005.10.036 -
Haiming, Z., Xiaoxiao, S. (2013) Study on prediction of Atmospheric
$PM_{2.5}$ based on RBF Neural Network. 4th International Conference on Digital Manufacturing and Automation, June, Qindao, Shandong, China. - Heckman, J.J. (1979) Sample Selection Bias as a Specification Error. Econometrica: Journal of the Econometric Society 153-161.
- Hornik, K.M. (1991) Approximation Capabilities of Multilayer Feed Forward Networks. Neural Networks 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
- Hornik, K.M. (1993) Some new Results on Neural Network Approximation. Neural Networks 6(8), 1069-1072. https://doi.org/10.1016/S0893-6080(09)80018-X
- Hornik, K.M., Stinchocombe, M., White, H. (1989) Multilayer Feed Forward Networks are Universal Approximators. Neural Networks 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
- International Agency for Research on Cancer (1988) Overall Evaluation of Carcinogenicity: An Update of IARC Monographs [IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, Suppl 7] IARC, Lyon France. Vol 1-44.
- Jo, W.K., Song, K.B. (2001) Exposure to Volatile Organic Compounds for Individuals with Occupations Associated with Potential Exposure to Motor Vehicle Exhaust and/or Gasoline Vapor Emissions. Science of the Total Environment 269(1-3), 25-37. https://doi.org/10.1016/S0048-9697(00)00774-9
- Kennedy, J.B., Neville, A.D. (1964) Basic Statistical Methods for Engineers and Scientists. 2nd Ed. Harper and Row, New York.
- Kohohen, T. (1984) Self-Organization and Associative Memory. New York: Springer-Verlag.
- Kurt, A., Gulbagci, B., Karaca, F., Alagha, O. (2008) An Online Air Pollution Forecasting System using Neural Networks. Environment International 34, 592-598. https://doi.org/10.1016/j.envint.2007.12.020
- Leshno, M., Lin, V.Y., Pinkus, A., Schocken, S. (1993) Multilayer Feed Forward Networks with a Nonpolynomial Activation Function can approximate any Function. Neural Networks 6(6), 861-867. https://doi.org/10.1016/S0893-6080(05)80131-5
- Maltoni, C., Ciliberti, A., Pinto, C., Soffritti, M., Belpoggi, F., Menarini, L. (1990) Results of Long Term Experimental Carcinogenicity Studies of the Effects of Gasoline, Correlated Fuels and Major Gasoline Aromatic in Rats, Ann. Clean Air Act Amendments: Part A, Section 112, pp. 101-549.
- Menhaj, M. (1998) Computational Intelligence, Fundamentals of Artificial Neural Networks, Vol. 1 Amirkabir University publisher, Tehran, Iran.
- Menhaj, M., Safpour (1998) Computational Intelligence, Application of Artificial Neural Networks, Vol. 2, professor Hasabi publisher, Tehran, Iran.
-
Moustris, K.P., Ziomas, I.C., Paliatsos, A.G. (2010) 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants
$NO_2$ , CO,$SO_2$ , and$O_3$ using a neural Networks in Athens, Greece. Water, Air and Soil Pollution 209, 29-43. https://doi.org/10.1007/s11270-009-0179-5 -
Mysteries, 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
$PM_{10}$ Levels in a Mediterranean City. Water, Air and Soil Pollution 224(1634), 3-11. - Noori, A., Eshraghi, A., Ajdarpour, A. (2013) Comparison of the ANN Neural Network with Multivariate Linear Regression Analysis for Perdiction of Daily Carbon Monoxide: A Case Study, Tehran. 7th national congress of civil engineering, Sistan and Zahedan University, Zahedan, Iran.
- Owega, S., Khan, B.U.Z., Evans, G.J., Jervis, R.E., Fila, M. (2006) Identification of Long-Range Aerosol Transport Patterns in Toronto Via Classification of Back Trajectories by Cluster Analysis and Neural Network Techniques. Chemometrics and Intelligent Laboratory Systems 83(1), 26-33. https://doi.org/10.1016/j.chemolab.2005.12.009
- Ozcan, H.K., Ucan, O.N., Sahin, U., Borat, M., Bayat, C. (2006) Artificial Neural Network Modeling of Methane Emissions at Istanbul Kemerburgaz-Odayeri Landfill site. Journal of Scientific and Industrial Research 65(2), 128-134.
- Ruiz-Suarez, J.C., Mayora-Ibarra, O.A., Torres-Jimenez, J., Ruiz-Suarez, L.G. (1995) Short-term Ozone Forecasting with Artificial Neural Networks. Advances in Engineering Software 23(3), 43-149.
- Song, X.M. (1996) Radial Basis Function Networks for Empirical Modeling of Chemical Process. MSc Thesis, University of Helsinki.
- Sousa, S.I.V., Martins, F.G., Pereira, M.C., Alvim-Ferraz, M.C.M. (2006) Prediction of Ozone Concentrations in Oporto City with Statistical Approaches. Chemosphere 64, 1141-1149. https://doi.org/10.1016/j.chemosphere.2005.11.051
- Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., Pereira, M.C. (2007) Multiple Linear Regression and Artificial Neural Networks based on Principal Components to Predict Ozone Concentrations. Environmental Modelling and Software 22, 97-103. https://doi.org/10.1016/j.envsoft.2005.12.002
- Sun, G., Hoff, S.J., Zelle, B.C., Nelson, M.A. (2008) Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis function network. Journal of Air and Waste Management 58, 1571-1576. https://doi.org/10.3155/1047-3289.58.12.1571
- Tasadduq, I., Rehman, S., Bubshait, K. (2002) Application of Neural Networks for the Prediction of Hourly Mean Surface Temperatures in Saudi Arabia. Renewable Energy 25(4), 545-554. https://doi.org/10.1016/S0960-1481(01)00082-9
- Wallace, L. (1990) Major Sources of Exposure to Benzene and other Volatile Organic Chemicals. Risk Anal 10, 59-64. https://doi.org/10.1111/j.1539-6924.1990.tb01020.x
- Willmott, C.J., Robeson, S.M., Matsuura, K. (2012). A refined Index of Model Performance. International Journal of Climatology 32(13), 2088-2094. https://doi.org/10.1002/joc.2419
- World Health Organization Regional Office for Europe (1989) Indoor Air Quality Organic Pollutants, Report on a WHO meeting. p. 56-71.
Cited by
- The prediction of atmospheric concentrations of toluene using artificial neural network methods in Tehran vol.4, pp.4, 2015, https://doi.org/10.12989/aer.2015.4.4.219
- Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City, Iran, by Using an Artificial Neural Network vol.25, pp.4, 2016, https://doi.org/10.1002/tqem.21464
- The Concentration of BTEX in the Air of Tehran: A Systematic Review-Meta Analysis and Risk Assessment vol.15, pp.9, 2015, https://doi.org/10.3390/ijerph15091837