To create a continuous surface or map of the phenomenon, predictions are made for locations in
the study area based on the semivariogram and the spatial arrangement of measured values that are
nearby. There are various kinds of interpolation techniques used in geostatistics. Those are divided
into two as stochastic and deterministic according to their model interpretation. Stochastic
interpolation techniques create surfaces incorporating the statistical properties of the measured data.
Because they are based on statistics, these techniques produce not only prediction surfaces but also
error or uncertainty surfaces, giving an indication of how good the predictions are. In this study,Kriging has been chosen as the stochastic interpolation method for the inundation mapping. Kriging is
similar to Inverse Distance Weighting which is an exact interpolation method, in that it weights the
surrounding measured values to derive a prediction for each location. However, the weights are based
not only on the distance between the measured points and the prediction location but also on the
overall spatial arrangement among the measured points (Zimmerman et al., 1999). Kriging also
forms weights from surrounding measured values to predict values at unmeasured locations and the
closest measured values usually have the most influence.