Wavelet analysis residual kriging vs. neural network residual kriging |
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| Authors: | V Demyanov S Soltani M Kanevski S Canu M Maignan E Savelieva V Timonin V Pisarenko |
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| Institution: | (1) Nuclear Safety Institute RAS, 52, B. Tulskaya, Moscow 113191, Russia, RU;(2) Technological University of Compiegne, France, FR;(3) Institut National des Sciences Appliquees (INSA), France, FR;(4) University of Lausanne, Switzerland, CH;(5) International Institute of Earthquake Prediction Theory and Mathematical Geophysics (MITPAN), Moscow, Russia, RU |
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| Abstract: | 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. |
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