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Last update: Ing. Luboš Matějíček, Ph.D. (13.03.2024)
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Last update: Ing. Luboš Matějíček, Ph.D. (13.03.2024)
Bequette, B.W., 1998. Process Dynamic: Modeling, Analysis, and Simulation. Prentice Hall, London, Sydney, Toronto, Tokyo. Bennet, B.S., 1995. Simulation Fundamentals. . Prentice Hall, London, Sydney, Toronto, Tokyo. Goodchild, M.F., 1996. GIS and Environmental Modeling: Progress and Research Issues. GIS World. Hannon, B., Ruth, M., 1997. Modeling Dynamic Biological Systems. Springer-Verlag, New York, Berlin, Heidelberg. Roughgarden, J., 1998. Primer of Ecological Theory. Prentice Hall, London, Sydney, Toronto, Tokyo. |
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Last update: Ing. Luboš Matějíček, Ph.D. (13.03.2024)
The course MO550P19 Environmental modeling is taught face-to-face or, in justifiable cases, by distance learning. Presentations of lectures and assignment of demo tasks are available on MOODLE: https://dl2.cuni.cz/course/view.php?id=1702 in pdf format. To be awarded credit, demo tasks must be completed and, in case of absence from the exercise, the required results must be submitted for review in MOODLE by the end of the grading period. The task is completed when more than 50 points are obtained in the range of 0 to 100. The recommended submission deadline is the next exercise. The exam takes the form of an electronic test in MOODLE (20 randomly selected questions with a choice of suitable answers). |
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Last update: Ing. Luboš Matějíček, Ph.D. (13.03.2024)
1. Systems theory: definition and delimitation of the system; static, dynamic and stochastic models; deductive and inductive identification when creating models; computer models; validation and verification; identification of model parameters, optimization. 2. Obtaining data within experiments: types of data; data accuracy; calibration, accuracy classes and measurement sensitivity; data management; examples of experimental procedures in the natural sciences. 3. Use of statistics and probability theory: types of data; location and variability characteristics; hypothesis testing. 4. Use of regression and correlation analysis, application of factor analysis: method of least squares; linear and non-linear regression; correlation analysis; data transformation; time lines; examples. 5. Approaches to modeling ecological systems: individual, population, community and ecosystem; use of physical laws and ecological rules; matter and energy flows. 7. Analysis and simulation of dynamic models: state variables and trajectories, equilibrium states and their stability; linear and non-linear dynamic models; numerical methods for calculating models; examples. 8. Examples of ecological models: modeling interactions in communities and ecosystems; models of matter and energy flows; simulation using computer programs ACSL, Mathematica, MATLAB-Simulink. 9. Analysis of spatial interactions of ecological systems: basic interactions of populations, in communities and in ecosystems; spatial models and their simulations; use of GIS and remote sensing; spatial statistical methods and interactions; discrete models. 10. Landscape analysis from the point of view of modeling: structure, corridors, networks; natural process analysis and risk assessment using GIS and remote sensing; modeling interactions in the landscape. 11. Contamination of environmental systems: compartment models and models with distributed parameters; diffusion modeling; numerical methods for solving models; examples. 12. System analysis of environmental systems and network analysis methods, linear programming, deterministic and stochastic models, chaos theory, use of neural networks, fractal theory. |