Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation. Part two: an intelligent system based on multi-classifier technique.

The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.

Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation part one: the applicability of a fuzzy rule based approach.

This work presents the applicability of applying a fuzzy logic approach to the calculation of noontime erythemal UV irradiance for the plain areas of Egypt. When different combinations of data sets were examined from the test performance point of view, it was found that 91% of the whole series was estimated within a deviation of less than +/-10 mW/m(2), and 9% of these deviations lay within the range of +/-15 mW/m(2) to +/-25 mW/m(2).

The relative influence of the anthropogenic air pollutants on the atmospheric turbidity factors measured at an urban monitoring station.

This work is based on simultaneous measurements of direct solar radiation along with other chemical measurements, with the objective of investigating the diurnal and seasonal variations of atmospheric turbidity factors (i.e., Linke's factor, Angström's coefficient, and aerosol optical depth).

Dependence of urban air pollutants on meteorology.

Dependence of air pollutants on meteorology is presented with the aim of understanding the governing processes pollutants phase interaction. Intensive measurements of particulate matter (PM10) and gaseous materials (e.g.