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Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining

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Transactions on Computational Collective Intelligence XXX

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 11120))

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Abstract

Prolonged climate change contributes to an increase in the local concentrations of O3 and PMx in the atmosphere, influencing the seasonality and duration of air pollution incidents. Air pollution in modern urban centers such as Athens has a significant impact on human activities such as industry and transport. During recent years the economic crisis has led to the burning of timber products for domestic heating, which adds to the burden of the atmosphere with dangerous pollutants. In addition, the topography of an area in conjunction with the recording of meteorological conditions conducive to atmospheric pollution, act as catalytic factors in increasing the concentrations of primary or secondary pollutants. This paper introduces an innovative hybrid system of predicting air pollutant values (IHAP) using Soft computing techniques. Specifically, Self-Organizing Maps are used to extract hidden knowledge in the raw data of atmospheric recordings and Fuzzy Cognitive Maps are employed to study the conditions and to analyze the factors associated with the problem. The system also forecasts future air pollutant values and their risk level for the urban environment, based on the temperature and rainfall variation as derived from sixteen CMIP5 climate models for the period 2020–2099.

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Authors and Affiliations

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

    Lazaros Iliadis & Konstantinos Demertzis

  2. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 193 Pandazidou st., 68200, N Orestiada, Greece

    Vardis-Dimitris Anezakis

  3. School of Engineering, Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias 12, 67100, Xanthi, Greece

    Stefanos Spartalis

Authors
  1. Lazaros Iliadis
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  2. Vardis-Dimitris Anezakis
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  3. Konstantinos Demertzis
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  4. Stefanos Spartalis
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Corresponding author

Correspondence to Vardis-Dimitris Anezakis .

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Editors and Affiliations

  1. Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, SA, Australia

    Richard Kowalczyk

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Iliadis, L., Anezakis, VD., Demertzis, K., Spartalis, S. (2018). Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining. In: Thanh Nguyen, N., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXX. Lecture Notes in Computer Science(), vol 11120. Springer, Cham. https://doi.org/10.1007/978-3-319-99810-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-99810-7_8

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