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Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration

J C M Pires1, B Gonçalves, F G Azevedo

  • 1LEPAE, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal. jcpires@fe.up.pt

Environmental Science and Pollution Research International
|March 3, 2012
PubMed
Summary

Genetic algorithms optimize artificial neural networks for predicting next-day hourly ozone concentrations. A two-regime threshold model using temperature, carbon monoxide, and nitrogen dioxide proved most effective for ozone forecasting.

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Last Updated: May 24, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Computational Intelligence

Background:

  • Predicting surface ozone (O(3)) concentrations is crucial for air quality management.
  • Artificial neural networks (ANNs) offer potential for complex environmental modeling.
  • Genetic algorithms (GAs) can optimize ANN architectures and parameters.

Purpose of the Study:

  • To develop and evaluate three methodologies for defining ANNs using GAs to predict hourly average surface ozone concentrations.
  • To explore threshold models where ozone behavior changes based on specific variables.
  • To identify key predictors for ozone formation in urban environments.

Main Methods:

  • Three distinct methodologies were proposed to define ANN models via GAs.
  • Two methodologies employed threshold models with two and four regimes, respectively.
  • Predictor variables included prior day's O(3), carbon monoxide (CO), nitrogen oxides (NOx), and meteorological data (temperature, solar radiation, humidity, wind speed).

Main Results:

  • Genetic algorithms were successfully applied to determine ANN activation functions and neuron counts.
  • In threshold models, temperature, CO, and nitrogen dioxide (NO(2)) were identified as key variables defining ozone regimes.
  • These variables are significant due to their role in urban atmospheric ozone chemistry.

Conclusions:

  • The developed threshold model with two regimes demonstrated the highest efficiency in predicting O(3) concentrations.
  • The study successfully optimized ANNs for air pollution forecasting using GAs.
  • The findings highlight the importance of specific chemical and meteorological factors in urban ozone dynamics.