Akademik Veri Yönetim Sistemi
INTERNATIONAL JOURNAL OF REMOTE SENSING, cilt.31, sa.2, ss.551-561, 2010 (SCI-Expanded)
In this study, we attempt to develop an ozone forecast model using two different approaches. The first approach is to use a multiple linear regression method and the second is to use a feed-forward artificial neural network. Models are developed for the ozone period of April through to September of the years 2002 and 2003 and verified for May to August 2004. In both models, 19 predictors are used. Calculated agreement indices (AI) for the model development period are 0.82 for the linear regression model and 0.88 for the artificial neural network model. On the other hand, AI values decrease to 0.53 and 0.64 for the validation period. Poor performance of the models in the validation phase might be due to the different maximum daily ozone averages of these two periods. While the average of maximum ozone values is 61.1 mu g m(-3) in the model development phase, it is 42.2 mu g m(-3) in the model validation phase.