Selection of an appropriate model to predict plume dispersion in coastal areas

In order to suggest a new methodology for selecting an appropriate dispersion model, various statistical measures having respective characteristics and recommended value ranges were integrated to produce a new single index by using fuzzy inference where eight statistical measures for various model results, including fractional bias (FB), normalized mean square error (NMSE), geometric bias mean (MG), geometric bias variance (VG), within a factor of two (FAC2), index of agreement (IOA), unpaired accuracy of the peak concentration (UAPC), and mean relative error (MRE), were taken as premise part variables. The new methodology using a single index was applied to the prediction of ground-level SO₂ concentration of 1-h average in coastal areas, where eight modeling combinations were organized with fumigation models, σ_y schemes for pre-fumigation, and modification schemes for σ_y during fumigation. As a result, the fumigation model of Lyons and Cole was found to have better predictability than the modified Gaussian model assuming that whole plume is immerged into the Thermal Internal Boundary Layer (TIBL). Again, a better scheme of σ_y (fumigation) was discerned. This approach, which employed the new integrated index, appears to be applicable to model evaluation or selection in various areas including complex coastal areas.