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Local maximum ozone concentration prediction using soft computing methodologies

Author
Gomez, P.; Nebot, A.; Ribeiro, S.; Alquezar, R.; Mugica, F.; Wotawa, F.
Type of activity
Journal article
Journal
Systems analysis, modeling, simulation
Date of publication
2003-08
Volume
43
Number
8
First page
1011
Last page
1031
DOI
https://doi.org/10.1080/0232929031000081244 Open in new window
URL
http://www.tandfonline.com/doi/abs/10.1080/0232929031000081244 Open in new window
Abstract
The prediction of ozone levels is an important task because this toxic gas can produce harmful effects to the population health especially of children. This article describes the application of the Fuzzy Inductive Reasoning methodology and a Recurrent Neural Network (RNN) approach, the Long Short Term Memory (LSTM) architecture, to a signal forecasting task in an environmental domain. More specifically, we have applied FIR and LSTM to the prediction of maximum ozone(O3) concentrations in the Eas...
Keywords
Air Pollution, Environmental Modeling, Fuzzy Inductive Reasoning, Long Short Term Memory, Ozone Concentration, Recurrent Neural Networks
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants