Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting
- 1 March 2012
- journal article
- research article
- Published by Springer Nature in Environmental Science and Pollution Research
- Vol. 19 (8) , 3228-3234
Abstract
This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O3) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons.Keywords
This publication has 42 references indexed in Scilit:
- Correction methods for statistical models in tropospheric ozone forecastingAtmospheric Environment, 2011
- Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, TurkeyCLEAN – Soil, Air, Water, 2010
- Evolutionary procedure based model to predict ground–level ozone concentrationsAtmospheric Pollution Research, 2010
- Neural networks and statistical techniques: A review of applicationsExpert Systems with Applications, 2009
- Audio signal processing by neural networksNeurocomputing, 2003
- Monte carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variablesAtmospheric Environment, 1998
- Forecasting with artificial neural networks:International Journal of Forecasting, 1998
- Acquiring the constitutive relationship for a thermal viscoplastic material using an artificial neural networkJournal of Materials Processing Technology, 1996
- Photochemical modeling of the Southern California air quality studyEnvironmental Science & Technology, 1993
- An analysis of surface ozone data measured at the end of the 19th century in Zagreb, YugoslaviaAtmospheric Environment. Part A. General Topics, 1991