Geoscience Reference
In-Depth Information
As mentioned above the item IDPDA analyses the environmental information
and in that way helps to assess real state of the nature/society subsystems within
concrete space-temporal boundaries. Here it is necessary to note that measurements
conducted by satellites or
flying laboratories deliver small volumes of statistically
reliable samples. Moreover, these data are usually non-stationary. The formation of
the input information in the GIMS based on monitoring data that received epi-
sodically in time and fragmentarily in space is a problem, which can be solved by
using speci
fl
c algorithms that are included in the item IDPDA. These algorithms are
described in Chap. 2 . Choice of one of them is realized automatically by item
IDPDA depending on the type of treated task. Large-scale tasks of cartographic
representation of monitoring data are carried out using spline or differential inter-
polation. In the case, when un-removable information uncertainty exists, then the
evolutionary modeling method is employed.
The method of differential approximation has universal character and as unique
method of the theory of approximation functions is often used for the analysis of
dynamic information. In the case when the task of data reconstruction within the
inter-trass space is performed, the method of differential approximation reduces this
task to that of the measurement data reduction to unique time. Moreover, coupled
use of the differential approximation and spline-approximation methods raises the
precision of task solution when there exist cross-trasses of satellites and
fl
flying
laboratories.
The study of the natural objects and processes using remote sensing methods has
promoted the evolutionary technology paradigm as a knowledge technology
directed towards the restored adaptation (Bukatova et al. 1991). This paradigm lies
in the GIMS technology base. Succession for this paradigm needs the improvement
of
taking into consideration of un-removable information uncer-
tainty that is practically always present in the global ecoinformatics. The evolu-
tionary modeling is such a super-intellect. It is a method of adaptive structural
identi
super-intellect
cation of objects, based on the synthesis of structured models using the
simulation mechanisms of natural evolution and their self-organization.
The evolutionary intellectual technology creates the model for nature-society
subsystem that appears an unexpected temporal change. This model guarantees the
elimination of information uncertainty at any time of monitoring. Figure 1.8
illustrates the main scheme for this technology. In the usual case, there are two
processes that are permanently alternated
the process of structural adaptation and
the process of usage. The arbitrary stage of adaptation is characterized by synthesis
of models that are used for forecasting, interpolation and other operations with
fragmentary data. The most ef
cient model is the choice from stage to stage of
adaptation. The procedure of evolutionary selection between models provides the
GIMS functioning that is practically unlimited in time when an un-removable
informational uncertainty is present.
The item CM performs informational
s
requirements and prepares operative informational bulletins following the changing
situations at all spatial scales. Cartographic identi
filling of the
final maps according to user
'
cation of the environmental
objects is realized in accordance to the schemes depicted in Figs. 1.2 and 1.3 .
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