@article{(Open Science Index):https://publications.waset.org/pdf/838,
	  title     = {Meteorological Data Study and Forecasting Using Particle Swarm Optimization Algorithm},
	  author    = {S. Esfandeh and  M. Sedighizadeh},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Environmental and Ecological Engineering},
	  volume    = {5},
	  number    = {2},
	  year      = {2011},
	  pages     = {108 - 110},
	  ee        = {https://publications.waset.org/pdf/838},
	  url   	= {https://publications.waset.org/vol/50},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 50, 2011},
	}