Syllabus
1. Theory of systems: definition and specification of systems; static, dynamic and stochastic models; deductive and inductive approach in the model development; computer models; validation and verification; identification of model parameters, optimization.
2. Experimental approach in data acquisition: experiment, classification of errors; data accuracy and precision; calibration, precision classes and sensitivity of measure instruments; data management; examples of experimental methods in natural science.
3. Using statistics and theory of probability: type of data; population and samples; measures of central tendency, dispersion and variability; probabilities and their characteristics; examples of data distribution; hypotheses and statistical tests; analysis of variance.
4. Using of regression and correlation analysis, applied factor analysis: the method of the least squares; linear and non-linear, simple and multiple regression; correlation analysis; data transformations; time series; examples of using factor analysis.
5. Concepts of modelling basic ecological systems: specification of the individual, population, community and ecosystem; using physical laws and ecological rules; matter and energy flows.
6. Models of population: estimates of basic parameters; discrete growth models; exponential and logistic growth; models with time delays of variables; Leslie models; basic interactions among populations; examples of models.
7. Analysis and simulation of dynamic models: state variables and trajectories, equilibrium and its stability; linear and non-linear dynamic models; numerical methods for digital simulation; examples.
8. Examples of ecological models: modelling of interactions in communities and ecosystems; models of matter and energy flows; simulation with computer programs ACSL, Mathematica, MATLAB-Simulink.
9. Analysis of spatial characteristics and interactions of ecological systems: basic spatial characteristics and interactions of populations, communities and ecosystems; spatial dynamic models and its simulation; using GIS and Remote Sensing; spatial statistical methods and interpolations; discrete models.
10. Analysis of landscape from the modelling perspective: structure, corridors, matrix and networks; analysis of natural processes and impact assessment with GIS and Remote Sensing; modelling of landscape interactions.
11. Contamination of environmental systems: compartment and distributed-parameter models; modelling of diffusion; numerical methods for digital simulation; examples of contamination of groundwater, surface water, air and soils; accumulation and transport of contaminants in biotic parts of ecosystems.
12. System analysis of environmental systems and network analysis, linear programming, deterministic and stochastic models, theory of chaos, using of neuron artificial networks, theory of fractals.
Annotation
The lecture is primarily focused on computer modelling in the area of living environment. For the students of natural science, the lecture sums up the introductory course that is extended by other optional lectures, exercises and seminars.
Presentations of individual aspects of modelling are derived from generally known principles. The lecture is complemented by computer demonstrations of selected problems, which are consecutively processed by students in the framework of exercises.