Abstract
All over the globe, major urban centers face a significant air pollution problem, which is becoming worse every year. This research effort aims to contribute towards real time monitoring of air quality, which is a target of great importance for people’s health. However, a serious obstacle is the high percentage of erroneous or missing data which is highly prolonged in many of the cases. To overcome this problem and due to the individuality of each residential area of Athens, separate local ANN had to be developed, capable of performing reliable interpolation of missing data vectors on an hourly basis. Also due to the need for hourly overall estimations of pollutants in the wider area of a major city, ANN ensembles were additionally developed by employing four existing methods and an innovative fuzzy inference approach.
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Bougoudis, I., Iliadis, L., Papaleonidas, A. (2014). Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_1
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DOI: https://doi.org/10.1007/978-3-319-11071-4_1
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