APPLICATION OF A NEURAL NET-AIR DISPERSION MODELLING ON THE INDIANAPOLIS URBAN DATA SET

In this work is presented the development of an integrated model composed by a Neural Net and a dispersion model. Using the concentrations predicted by an air dispersion model (ADMD) as input to a Neural net, we evaluated the performances of this new methodology in the cases of a releases from an elevated emission source using the urban data set of the Indianapolis field study as test case. INTRODUCTION The air pollution models constitute a sophisticated tools that reflects the knowledge on turbulent transport in the atmosphere. However, this models have not so far been able to reproduce satisfactorily ground level concentrations because the influence of important variables is not perfectly described. The results they provide are affected by a considerable margin of error, the most important of which is the uncertainty linked to the intrinsic variability of the atmosphere and to local topography. Most operative models for estimating the dispersion of gases and particles in the atmospheric boundary layer are based on the Gaussian approach. Such models cannot properly simulate complex non homogeneous conditions in a three-dimensional and K-models are widely used in the field of air pollution studies. In reference to the prediction of complex systems it is well known that neural networks can work as universal approximators of non-linear functions and, consequently, can be used in assessing the dynamics of such systems (to take account of the available experimental data). Usually, they have become a useful tool either where no precise phenomenological model is available or when uncertainty in input and output signals complicates the application of deterministic modelling as, for example, in environmental systems. The NN applications in atmospheric systems have been used for short term forecasting since the early nineties (Boznar et al., 1993), when a model was constructed to predict atmospheric sulphur dioxide in a polluted industrialized area of Slovenia. Other works have reported the use of NN for forecasting daily maximum ozone levels as Comrie (1997) in various urban areas, using average daily meteorological data as input parameters. A critical review of the NN applications in atmospheric science has been attempted by Gardner et al. (1998) whose comparison among models turns out to be rather unbalanced, since each model was trained with different kinds of data. The proposed approach relies on the development of an integrated model that optimises the performances of each methodology (air dispersion models and NN). The concentrations evaluated by an air pollution model are coupled with a Neural Net (NN), so as to adjust the influence of important variables on dispersion models (which may produce systematic underor over-prediction of measured concentrations) and, contemporaneously, to minimize the input neural net parameters. In particular, an optimised 3-Layer Perception with error-backpropagation learning rules is used to filter the air pollution concentrations evaluated using an operative analytical non-Gaussian model (ADMD) that takes account of the vertical profiles of wind, of the turbulent diffusivity. We evaluated the performances of this methodologies in the cases of a releases form an elevated emission source using the urban data set of the Indianapolis field study.