Predicting PM10 Concentrations Using Fuzzy Kriging

The prediction of meteorological phenomena is usually based on the creation of surface from point sources using the certain type of interpolation algorithms. The prediction standardly does not incorporate any kind of uncertainty, either in the calculation itself or its results. The selection of the interpolation method, as well as its parameters depend on the user and his experiences. That does not mean the problem necessarily. However, in the case of the spatial distribution modelling of potentially dangerous air pollutants, the inappropriately selected parameters and model may cause inaccuracies in the results and their evaluation. In this contribution, we propose the prediction using fuzzy kriging that allows incorporating the experts knowledge. We combined previously presented approaches with optimization probabilistic metaheuristic method simulated annealing. The application of this approach in the real situation is presented on the prediction of PM10 particles in the air in the Czech Republic.