Air Pollution Prediction By Artificial Neural
Networks
Author(s)
M.I.V. Furtado & N.F.F. Ebecken
Abstract
In this work, an artificial neural network strategy is presented to predict
suspended paniculate matter in the atmospheric air. The considered area is the
Rio de Janeiro metropolitan region, a brazilian urban center. The topography,
the nonuniform occupation, the Guanabara Bay and the meteorological
peculiarities contribute to the need of a complex model. Backpropagation,
generalized regression neural networks and genetic algorithms are used to
evolve neural network structures to search for significant input variables and
maximize the predictive accuracy of the results. The input data corresponds to
14 meteorological variables on different locations. The predicted values are
satisfactory and the methodology presented promises perspectives.
1 Introduction
Artificial Neural Networks are a mathematical paradigm ins
Keywords
Related Book
Other papers in this volume
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