Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30835
Meteorological Data Study and Forecasting Using Particle Swarm Optimization Algorithm

Authors: S. Esfandeh, M. Sedighizadeh


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.

Keywords: Climate, Meteorological, Weather, prediction, PSO

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1722


[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.