Affiliations: Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Orestiada, Greece
Correspondence:
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Corresponding author: Lazaros Iliadis, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 193 Pandazidou st., N Orestiada 68200, Greece. E-mail:[email protected]http://filab.fmenr.duth. gr
Abstract: Air pollution is the problem of adding harmful substances
or other agents into the atmosphere and it is caused by industrial,
transport or household activities. It is one of the most serious problems of
our times and the determination of the conditions under which we have
extreme pollutants' values is a crucial challenge for the modern scientific
community. The innovative and effective hybrid algorithm designed and
employed in this research effort is entitled Easy Hybrid Forecasting (EHF).
The main advantage of the EHF is that each forecasting does not require
measurements from sensors, other hardware devices or data that require the
use of expensive software. This was done intentionally because the
motivation for this work was the development of a hybrid application that
can be downloaded for free and used easily by everyday common people with no
additional financial cost, running in devices like smart phones. From this
point of view it does not require data from sensors or specialized software
and it can offer people reliable information about extreme cases.
Keywords: Fuzzy C-means, neural gas, self-organizing maps, feed forward neural networks, random forest, air pollution, feature selection
