Advances in Airborne Pollution Forecasting Using Soft Computing Techniques

Antonio, Aceves-Fernandez Marco; Sotomayor-Olmedo, Artemio; Gorrostieta-Hurtado, Efren; Carlos, Pedraza-Ortega Jesus; Manuel, Ramos-Arreguín Juan; Sandra, Canchola-Magdaleno; Emilio, Vargas-Soto

doi:10.5772/16273

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2014

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“…A FIS is the main unit of a fuzzy logic system, the part where the decision is made, by constructing appropriate rules based on the theory of fuzzy sets. The rules are constructed linguistically (IF-THEN-) having the general form of "If A Then B" where A and B are (collections of) propositions containing linguistic variables linked by connectors (AND, OR) to make the correct decision-making [7].…”

Section: Introductionmentioning

confidence: 99%

Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms

Martinez-Zeron¹,

Aceves-Fernández²,

Gorrostieta-Hurtado³

et al. 2014

IJIS

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This contribution shows the feasibility of improving the modeling of the non-linear behavior of airborne pollution in large cities. In previous works, models have been constructed using many machine learning algorithms. However, many of them do not work for all the pollutants, or are not consistent or robust for all cities. In this paper, an improved algorithm is proposed using Ant Colony Optimization (ACO) employing models created by a neuro-fuzzy system. This method results in a reduction of prediction error, which results in a more reliable prediction models obtained.

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Section: Introductionmentioning

confidence: 99%