An Outlook of Ozone Air Pollution through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity ModelsAn Outlook of Ozone Air Pollution Through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity Models
Air pollution and atmospheric ozone can cause damages to human health and to the environment. This study explores the potential approach of the artificial neural network (ANN) model and compares it with a regression model for predicting ozone concentration using different parameters and functions measured by the Climate Prediction Center of US National Weather Service. In addition, this study has compared the economic viability of ANN and other measuring methods. Results showed that the ANN-based model exhibited better performance. Such model types can be beneficial to government agencies. By predicting ozone concentration government agencies can take preventive measures to avoid significant health effects, protect local populations, and help preserve a sustainable environment.
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