Handling spatial data uncertainty using a fuzzy geostatistical approach for modelling methane emissions at the island of Java']Java

Handling uncertain spatial data and modelling of spatial data quality and data uncertainty are currently major challenges in GIS. Geodata usage is growing, for example in agricultural and environmental models. If the data are of a low quality, then model results will be poor as well. An important issue to address is the accuracy of GIS applications for model output. Spatial data uncertainty models, therefore, are necessary to quantify the reliability of model results. In this study we use a combination of fuzzy methods within geostatistical modelling for this purpose. The main motivation is to jointly handle uncertain spatial and model information. Fuzzy set theory is used to model imprecise variogram. parameters. Kriging predictions and kriging variances are calculated as fuzzy numbers, characterized by their membership functions. Interval width of predictions measures the effect of variogram uncertainty. The methodology is applied on methane (CH4) emissions at the Island of Java. Kriging standard deviations ranged from 12 to 26.45, as compared to ordinary kriging standard deviations, ranging from 12 to 33.11. Hence fuzzy kriging is considered as an interesting method for modeling and displaying the quality of spatial attributes when using deterministic models in a GIS.