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A CNN Adaptive Model to Estimate PM10 Monitoring
2006
In this work we introduce a model for studying the distribution and control of atmospheric pollution from PM10. The model is based on the use of a cellular neural network (CNN) and more precisely on the integration of the mass-balance equation; at the same time it simulates the scenario regarding a planar grid describing the whole studied area (the city of Palermo) by means of a CNN and a set of Bayesian networks. The CNN allows us to define a grid system whose dynamic evolution is a redefinition of the diffusion equation that considers contributions coming from near cells for each element of the grid. Dynamics of each cell is influenced by meteorological effects and by parameters related t…