Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic
This multidisciplinary research analyzes the atmospheric pollution conditions of two different places in Czech Republic. The case study is based on real data provided by the Czech Hydrometeorological Institute along the period between 2006 and 2010. Seven variables with atmospheric pollution information are considered. Different Soft Computing models are applied to reduce the dimensionality of this data set and show the variability of the atmospheric pollution conditions among the two places selected, as well as the significant variability of the air quality along the time.
KeywordsArtificial neural networks soft computing meteorology statistical models environmental conditions
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