Meteorological Data Study and Forecasting Using Particle Swarm Optimization Algorithm

Weather systems use enormously complex combinations of numerical tools for study and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become insufficient mostly because they lack adaptation. Therefore, the weather forecast problem is matched for heuristic approaches, such as Evolutionary Algorithms. Experimentation with heuristic methods like Particle Swarm Optimization (PSO) algorithm can lead to the development of new insights or promising models that can be fine tuned with more focused techniques. This paper describes a PSO approach for analysis and prediction of data and provides experimental results of the aforementioned method on realworld meteorological time series.




References:
[1] P.J Hurley, A. Blockley, K. ayner , Verification of a prognostic
meteorological and air pollution model for year-long predictions in the
Kwinana industrial region of Western Australia, Author, Title of the
Paper, Atmospheric Environment, Volume 35, Issue 10, April 2001,
Pages 1871-1880
[2] G. Liu, C. Hogrefe, S. Trivikrama Rao, Evaluating the performance of
regional-scale meteorological models: effect of clouds simulation on
temperature prediction, Atmospheric Environment, Volume 37, Issue 11,
April 2003, Pp: 1425-1433
[3] D. Heimann, E. M. Salomons, Testing meteorological classifications for
the prediction of long-term average sound levels, Applied Acoustics,
Volume 65, Issue 10, October 2004, Pp: 925-950
[4] G. Sistla, N. Zhou, W. Hao, J.-Y. Ku, S.T. Rao, R. Bornstein, F.
Freedman, P. Thunis, Effects of uncertainties in meteorological inputs
on urban airshed model predictions and ozone control strategies,
Atmospheric Environment, Volume 30, Issue 12, June 1996, Pp:2011-
2025
[5] Bautu , E. Bautu, " Meteorological Data Analysis and Prediction by
Means of Genetic Programming," Proceedings of the Fifth Workshop on
Mathematical Modeling of Environmental and Life Sciences Problems
Constant┬©a, Romania, September, 2006, pp. 35-42
[6] J. Kennedy, R. C. Eberhart, Y. Shi, Swarm Intelligence, 2001, Pages
369-392
[7] J.M. Guti'errez, R. Cano, A.S. Cofi'no and C. Sordo. Redes
Probabilsticas y Neu-ronales en las Ciencias Atmosf'ericas, Monografas
del Instituto Nacional de Me-teorologa, Madrid, 2004.