Geoscience Reference
In-Depth Information
An important structural feature of the GIMS is that it is constituted by the global
simulation model for nature/society system that controls information
uxes between
global databases and the GIMS database protected from the exaggeration and
uncertainty at
fl
truly global model plays the role of context
information for the GIMS workspace. Hierarchical structure of this workspace is
determined by series of directories and
the same time.
ʑ
guration that is
identical to structure shown in Figs.
1.3
and
1.4
. Subsystem of intelligent support
helps user to overcome isolated virtually unsolved questions displaying all the
questionable features of the studied problem. One of such unsolved question is the
choice of an adequate model. Choice of the model type done at the
files stored in the con
first stage of the
GIMS synthesis is often a poorly formalized procedure. In this case, it is required to
run the following stages:
primary informal description of the monitoring object;
formulation of the objectives for investigation;
substantial analysis of
the a priori
information about
the environmental
subsystem;
development of
the mathematical
(formalized) model
for
the subsystem
functioning;
set-up of the modelling algorithm;
synthesis of software package for the model run in a computer;
model veri
cation and evaluation of its parameters;
choice of the alternative versions of the model to adapt to the structure of the
GIMS database;
elaboration of scenarios on simulation experiments that re
fl
ect the objectives of
the research model; and
utilization of the model in an operational monitoring system.
Synthesis of the model as part of the GIMS item requires setting boundaries of
the object monitoring. This stage includes this model and models of more high
levels that parameterize the environment beyond those boundaries. The number of
the model levels depends on the spatial resolution of the monitoring system. The
model must be equipped with the following functions:
1. The measuring function that uses similarity of the model and the object to be
modeled.
2. The descriptive function that characterizes the object features in its various
states.
3. The interpretative function that describes the power of the model adaptability
and the applications of its results.
4. The explanatory function which represents the model ability for the interpre-
tation of the monitoring data in accordance with the terms of formal tools of
mathematical methods used in the model.
5. The prognostic function which is associated with the probability to forecast the
evolution of the environmental subsystem with given precision and for condi-
tions under which it is not observed.
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