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FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens

  • S.I. : EANN 2016
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.

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Authors and Affiliations

  1. Institute of Environmental Physics, DOAS Group, University of Bremen, Otto-Hahn-Allee 1, 28359, Bremen, Germany

    Ilias Bougoudis

  2. Lab of Forest Informatics, Democritus University of Thrace, 193 Pandazidou st., 68200, Orestiada, Greece

    Konstantinos Demertzis, Vardis-Dimitris Anezakis & Antonios Papaleonidas

  3. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, University Campus, Kimmeria, 67100, Xanthi, Greece

    Lazaros Iliadis

Authors
  1. Ilias Bougoudis
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  2. Konstantinos Demertzis
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  3. Lazaros Iliadis
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  4. Vardis-Dimitris Anezakis
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  5. Antonios Papaleonidas
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Correspondence to Konstantinos Demertzis.

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Bougoudis, I., Demertzis, K., Iliadis, L. et al. FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens. Neural Comput & Applic 29, 375–388 (2018). https://doi.org/10.1007/s00521-017-3125-2

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  • DOI: https://doi.org/10.1007/s00521-017-3125-2

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