The paper presents some contemporary approaches to spatial environmental
data analysis. The main topics are concentrated on the decision-oriented
problems of environmental spatial data mining and modeling: valorization
and representativity of data with the help of exploratory data analysis,
spatial predictions, probabilistic and risk mapping, development and
application of conditional stochastic simulation models. The innovative
part of the paper presents integrated/hybrid model-machine learning (ML)
residuals sequential simulations-MLRSS. The models are based on
multilayer perceptron and support vector regression ML algorithms used
for modeling long-range spatial trends and sequential simulations of the
residuals. NIL algorithms deliver non-linear solution for the spatial
non-stationary problems, which are difficult for geostatistical
approach. Geostatistical tools (variography) are used to characterize
performance of ML algorithms, by analyzing quality and quantity of the
spatially structured information extracted from data with ML algorithms.
Sequential simulations provide efficient assessment of uncertainty and
spatial variability. Case study from the Chernobyl fallouts illustrates
the performance of the proposed model. It is shown that probability
mapping, provided by the combination of ML data driven and
geostatistical model based approaches, can be efficiently used in
decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.