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Local maximum ozone concentration prediction using soft computing methodologies

Autor
Gómez, P.; Nebot, M.; Ribeiro, S.; Alquezar, R.; Mugica, F.; Wotawa, F.
Tipus d'activitat
Article en revista
Revista
Systems analysis, modeling, simulation
Data de publicació
2003-08
Volum
43
Número
8
Pàgina inicial
1011
Pàgina final
1031
DOI
https://doi.org/10.1080/0232929031000081244 Obrir en finestra nova
URL
http://www.tandfonline.com/doi/abs/10.1080/0232929031000081244 Obrir en finestra nova
Resum
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...
Paraules clau
Air Pollution, Environmental Modeling, Fuzzy Inductive Reasoning, Long Short Term Memory, Ozone Concentration, Recurrent Neural Networks
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants