Employing ANN That Estimate Ozone in a Short-Term Scale When Monitoring Stations Malfunction
This paper describes the design, development and application, of an intelligent system (operating dynamically in an iterative manner) capable of short term forecasting the concentration of dangerous air pollutants in major urban centers. This effort is the first phase of the partial fulfillment of a wider research project that is related to the development of a real time multi agent network serving the same purpose. Short term forecasting of air pollutants is necessary for the proper feed of the real time multi agent system, when one or more sensors are damaged or malfunctioning. From this point of view the potential outcome of this research is very useful towards real time air pollution monitoring. A vast volume of actual data vectors are combined from several measurement stations located in the center of Athens. The final target is the continuous estimation of Ozone (O3) in the historical city center, considering the effect of primitive pollutants and meteorological conditions from neighboring stations. A group comprising of hundreds artificial neural networks has been developed, capable of estimating effectively the concentration of O3 at a specific temporal point and also after 1, 2, 3 and 6 hours.
KeywordsArtificial Neural Network Ozone Concentration Input Neuron Major Urban Center Vast Volume
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