Connection between pollutants and micrometeorological and turbulence variables by using AI - CI modelling approaches
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The focus of this joint session concerns the application of Artificial Intelligence/Computational Intelligence techniques to predicting the spatial and temporal variation of pollutant, meteorological and turbulence fields. The AI/CI model can viewed as alternative to diagnostic and prognostic approaches, where the numerical convergence is well established according to the scale of the simulation. If we identify the diagnostic and prognostic model as deterministic modeling type, the AI/CI can considered as an intelligent method, where non-linear correlation can be deduced by the direct analysis of the measurement data. While for deterministic modeling the knowledge is inside the different scales of the simulation, in intelligent methods we can reproduce the actual response of the main meteorological data to all scales (by means of the training phase). The most important question related to AI/CI modeling concerns how can the physics of meteorological and turbulence variables be modeled. In particular, an interesting question could be how well the AI/CI methods reproduce spatial and temporal variation in these fields. Up to now, this problem is an open research question and the AI/CI can contribute to increasing the convergence of dynamical and spatial-temporal reconstruction of meteorological and pollutants data.
10th Conf on Artificial Intelligence Applications to Environmental Science
17th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA