Modern geological maps are 3D, or even 4D models representing the complex structure of the Earth. They interpret a variety of geological information and represent it in graphical form.

It is presented in detail how the selection of a structural model describing the behaviour of a steel hall transverse frame when subject to fire exposure in a more or less complex way may affect the fire resistance evaluation for such a frame. In the examples compiled in this paper the same typical one-aisle and single-story steel hall is subjected to simulated fire action, each time following the same fire development scenario.Aresultant fire resistance is identified individually in each case, using various computational models, on an appropriate static equilibrium path obtained numerically. The resulting estimates vary, not only in the quantitative sense, but also in terms of their qualitative interpretation. It is shown that the greater the simplification of the model used, the more overstated the estimated fire resistance is in relation to its real value. Such an overestimation seems to be dangerous to the user, as it gives him an illusory but formally unjustified sense of the guaranteed safety level.

The paper deals with recent developments in the fields of applied mathematics and operational research triggered by the needs of effective support of environmental policy-making processes that require interdisciplinary science-based advice. Mathematical models developed for this purpose demand new modeling paradigms for an adequate integration of pertinent knowledge. and creation of knowledge needed for rational decision-making. The article first summarizes the model-based support for problem solving from the point of view of actual decision-makers. Next, il discusses the model representation of the knowledge pertinent lo a given decision-making problem, and the recently developed modeling technology supporting the whole process of modeling complex problems. The last parł deals with novel methods and tools for integrated management of risks related to natural catastrophes. The presented methodology is illustrated by its application lo actual environmental policy-making support.