The paper presents an approach for mapping of precipitation data.
The main goal is to perform spatial predictions and simulations of
precipitation fields using geostatistical methods (ordinary kriging,
kriging with external drift) as well as machine learning algorithms
(neural networks). More practically, the objective is to reproduce
simultaneously both the spatial patterns and the extreme values.
This objective is best reached by models integrating geostatistics
and machine learning algorithms. To demonstrate how such models work,
two case studies have been considered: first, a 2-day accumulation
of heavy precipitation and second, a 6-day accumulation of extreme
orographic precipitation. The first example is used to compare the
performance of two optimization algorithms (conjugate gradients and
Levenberg-Marquardt) of a neural network for the reproduction of
extreme values. Hybrid models, which combine geostatistical and machine
learning algorithms, are also treated in this context. The second
dataset is used to analyze the contribution of radar Doppler imagery
when used as external drift or as input in the models (kriging with
external drift and neural networks). Model assessment is carried
out by comparing independent validation errors as well as analyzing
data patterns.