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.

[1]  Hisao Ishibuchi,et al.  Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks , 2001, Fuzzy Sets Syst..

[2]  Renkuan Guo,et al.  Predicting air pollution using fuzzy membership grade Kriging , 2007, Comput. Environ. Urban Syst..

[3]  Mikhail Kanevski,et al.  Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks , 2013 .

[4]  B. Brunekreef,et al.  Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). , 2013, The Lancet. Oncology.

[5]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[6]  W. E. Kelly,et al.  Imprecise (fuzzy) information in geostatistics , 1988 .

[7]  Bruce Denby,et al.  Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale , 2008 .

[8]  Mikhail F. Kanevski,et al.  Air Pollution Mapping Using Nonlinear Land Use Regression Models , 2014, ICCSA.

[9]  Didier Dubois,et al.  Kriging with Ill-Known Variogram and Data , 2010, SUM.

[10]  John S. Gulliver,et al.  Comparative assessment of GIS-based methods and metrics for estimating long-term exposures to air pollution , 2011 .

[11]  Roozbeh Shad,et al.  Predicting air pollution using fuzzy genetic linear membership kriging in GIS , 2009, Comput. Environ. Urban Syst..

[12]  Michael Hanss,et al.  The transformation method for the simulation and analysis of systems with uncertain parameters , 2002, Fuzzy Sets Syst..

[13]  Didier Dubois,et al.  Ranking fuzzy numbers in the setting of possibility theory , 1983, Inf. Sci..

[14]  P. Diamond.  Fuzzy kriging , 1989 .

[15]  Edzer Pebesma,et al.  Mapping of background air pollution at a fine spatial scale across the European Union. , 2009, The Science of the total environment.

[16]  A. Bárdossy,et al.  Kriging with imprecise (fuzzy) variograms. I: Theory , 1990 .

[17]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[18]  Alfred Stein,et al.  Handling Spatial Data Uncertainty Using a Fuzzy Geostatistical Approach for Modelling Methane Emissions at the Island of Java , 2004, SDH.

[19]  Didier Dubois,et al.  A fuzzy interval analysis approach to kriging with ill-known variogram and data , 2012, Soft Comput..

[20]  G. Heuvelink,et al.  A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .

[21]  Jiri Dvorský,et al.  Querying on Fuzzy Surfaces with Vague Queries , 2013, HAIS.

[22]  Didier Dubois,et al.  Kriging and Epistemic Uncertainty: A Critical Discussion , 2010, Methods for Handling Imperfect Spatial Information.