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Air pollution forecasting model control

Domańska D., Wojtylak M.

Journal of Medical Informatics & Technologies

2010

Vol. 14

9--22

In the paper we discuss the analysis of multidimensional data. We consider the relationship between them using a special fuzzy number form. Calculations are kept on set of actual and historical meteorological data. Our model using to forecast pollution concentrations is important in today because pollutions have very big influence on our life in particular pollutions PM10 (particulate matter less than 10 µm in diameter). The effects of inhaling particulate matter have been widely studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. Because of the size of the particle, they can penetrate the deepest part of the lungs. In Air Pollution Forecasting Model for the chosen weather forecast we find similar weather forecasts. Next, we find real meteorological situations from the historical data which correspond to them and we create fuzzy numbers, that is, the fuzzy weather forecasts. Then we estimate the validity of the weather forecast on the basis of the historical data and its accuracy. We investigate it with the help of a set of indicators, which corresponds to the parameters of the weather forecast, using the similarities rule of the weather forecast to the meteorological situation, a proper distance and data analysis. This comprehensive analysis allows us to investigate the effectiveness of forecasting pollution concentrations, putting the dependence between particular attributes describing the weather forecast in order and proving the legitimacy of the applicable fuzzy numbers in air pollution forecasting. Models are created for data, which are measured and forecasting in Poland. By reason of this data our models are testing in real sets of data and effects are received in active system.

Fuzzy image processing : a review and comparison of methods

Tzafestas S., Raptis S.

Image Processing & Communications

1999

Vol. 5, no 1

3--24

This paper presents a comprehensive review of fuzzy image processing methods. Specifically the following image processing problems are considered: (i) Image comprehension. (ii) image segmentation, (iii) image classification, (iv) image analysis, (v) image filtering, (vi) image understanding. In practice, an image cannot always be interpreted always exactly and perfectly. This is due to the existence of noise or the way the image is obtained, or, finally, to incorrect understanding of the image information content. These difficulties can be faced successfully through fuzzy logic and fuzzy reasoning. The field of image processing via fuzzy logic was initialed after Zadeh's 1965 seminar paper and is still expanding with new important results and applications. This paper is devoted to the treatment of still images, but some of the methods can be extended to the case of moving (video) images. The methods considered are critically discussed. Finally, a comparison of the effectiveness of (i) c-means, (ii) classical c-means, and (iii) adaptive clustering algorithms is made.