SubjectsSubjects(version: 941)
Course, academic year 2022/2023
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Environmental modelling - MO550P19E
Title: Environmental modelling
Guaranteed by: Institute for Environmental Studies (31-550)
Faculty: Faculty of Science
Actual: from 2021
Semester: winter
E-Credits: 4
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: 20
Min. number of students: 1
For 4EU+ students: no
Virtual mobility / capacity: no
State of the course: taught
Language: English
Additional information:
Note: enabled for web enrollment
Guarantor: Ing. Luboš Matějíček, Ph.D.
Teacher(s): Ing. Luboš Matějíček, Ph.D.
Annotation -
Last update: Ing. Luboš Matějíček, Ph.D. (25.10.2019)
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.
Literature -
Last update: Ing. Luboš Matějíček, Ph.D. (25.10.2019)

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.

Requirements to the exam -
Last update: Ing. Luboš Matějíček, Ph.D. (28.09.2020)

News (2020/SEP28)

Subject MO550P19 Environmental modelling will be taught in a distance-based way due to current developments in the health situation. Presentations of lectures and demo assignments will be available on MOODLE in pdf format.

For online presentations and consultations, the easiest use seems to be Google Meet and Microsoft Teams.

As part of the tutorial, we will primarily use Office 365, MATLAB Online, RStudio Cloud and other available SW for demo demonstrations. Where necessary, an account will be set up for your access.

Officially, we would start classes on schedule on Tuesday, 6.10. 2020 at 9:00, where we will discuss other organisational matters (via e-mail in SIS I will previously send you the access code for a mass online meeting).

To do the subject, you will need to develop a selected demo job. There will be ample time for the actual processing of the tasks and they will not be directly tied to subsequent online meetings.

The initial prerequisites for graduating from the subject are an indicative knowledge of working with a computer at the secondary school level.

Should there be a problem with the availability of suitable computing, demo jobs can be individually processed in the GIS Lab classroom.


Original information

1. The report focused on a selected theme.
2. The particular results of exercises.

Note: Subject can be graduated by distance teaching using MOODLE

Syllabus -
Last update: Ing. Luboš Matějíček, Ph.D. (25.10.2019)

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.

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