International Journal of Soft Computing

Year: 2013
Volume: 8
Issue: 4
Page No. 305 - 312

Forecasting Criteria Air Pollutants Using Data Driven Approaches: An Indian Case Study

Authors : S. Tikhe Shruti, K.C. Khare and S.N. Londhe

References

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000. Artificial neural networks in hydrology. I: Preliminary concepts. J. Hydrol. Eng., 5: 115-123.
CrossRef  |  Direct Link  |  

Barai, S.V., A.K. Dikshit and S. Sharma, 2007. ANN for air quality prediction: A comparative study. J. Soft comput. Ind. Appl., 39: 290-305.

Bose, N.K. and P. Liang, 2000. Neural Network Fundamentals with Graphs, Algorithms and Applications. Tata McGraw-Hill Publishing Company Limited, New Delhi.

Gardner, M.W. and S.R. Dorling, 1999. Artificial neural networks: The multilayer perceptron: A review of applications in the atmospheric sciences. J. Atmos. Environ., 32: 2627-2636.

Grivas, G. and A. Chaloulakou, 2006. Artificial neural network models for prediction of PM10 hourly concentrations, in the greater area of Athens, Greece. Atmos. Environ., 40: 1216-1229.
CrossRef  |  Direct Link  |  

Khare, M. and S.A. Nagendra, 2007. Artificial neural networks in vehicular pollution modelling. J.Studies Comput. Intell., 41: 41-45.

Koza, J.R., 1992. Genetic Programming on the Programming of Computers by Means of Natural Selection. MIT Press A Bradford Book, USA.,.

Londhe, S.N., 2008. Soft computing approach for real-time estimation of missing wave heights. J. Ocean Eng., 35: 1080-1089.
CrossRef  |  Direct Link  |  

Lu, W.Z., W.J. Wang, X.K. Wang, S.H. Yan and J.C. Lam, 2004. Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, HongKong. Environ. Res., 96: 79-87.
PubMed  |  Direct Link  |  

Nagendra, S.M. and M. Khare, 2006. Artificial neural network approach for modeling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol. Modell., 190: 99-115.
CrossRef  |  Direct Link  |  

Niska, H., T. Hiltunen, A. Karppinen, J. Ruuskanen and M. Kolehmainena, 2004. Evolving the neural network model for forecasting air pollution time series. Eng. Applied Artif. Intel., 17: 159-167.
CrossRef  |  

Perez, P. and J. Reyes, 2006. An integrated neural network model for PM10 forecasting. Atmosp. Environ., 40: 2845-2851.
CrossRef  |  Direct Link  |  

Pires, J.C.M., Alvim-Ferraz, M.C.M., M.C. Pariera and F.G. Martins, 2010. Prediction of PM10 concentration through Multigene genetic programming. Atmos. Pollut. Res., 1: 305-310.
Direct Link  |  

Pires, J.C.M., M.C.M. Alvim-Ferraz, M.C. Pariera and F.G. Martins, 2011. Prediction of troposphere ozone concentration: Application of a methodology based on Darwin's Theory of Evolution. Expert Syst. Appl., 38: 1903-1908.
CrossRef  |  

Raimondo, G., M. Alfonso and M.A. Walter, 2007. Machine learning tool to forecast PM10 level. Proceedings of the 11th International Conference on knowledge based and Intelligent Information and Engineering Systems, September 12-14, 2007, Vietri Sul Mare, Italy. -.

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