This paper deals with the problem of spatial data mapping. A new method
based on wavelet interpolation and geostatistical prediction (kriging)
is proposed. The method - wavelet analysis residual kriging (WARK) - is
developed in order to assess the problems rising for highly variable
data in presence of spatial trends. In these cases stationary prediction
models have very limited application. Wavelet analysis is used to model
large-scale structures and kriging of the remaining residuals focuses on
small-scale peculiarities. WARK is able to model spatial pattern which
features multiscale structure. In the present work WARK is applied to
the rainfall data and the results of validation are compared with the
ones obtained from neural network residual kriging (NNRK). NNRK is also
a residual-based method, which uses artificial neural network to model
large-scale non-linear trends. The comparison of the results
demonstrates the high quality performance of WARK in predicting hot
spots, reproducing global statistical characteristics of the
distribution and spatial correlation structure.