Environmental modeling

1.

Subject title

Environmental modeling

Еколошко моделирање

2.

Code

EI-I-02

3.

Study program

Eco-informatics, Cloud Computing, Data science in computer science and engineering, Bioinformatics, Security, Cryptography and Coding, IT management, Еducation with ICT, Software Engineering, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Коста Митрески

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The choice, use and prediction of environmental models

11.

Subject content:

1) Introduction to Environmental Modeling 2) indentation of application models; The student will get knowledge in developing environmentally friendly models that can be divided into empirical, Dynamic and mixed models. - Empirical models are constructed on the basics of the relationship between Different parameters. - Dynamic models are derived from the analysis of the links of the environmental and Boology analyzes that are based on calculations using differential equations. Some of the models strive to give an overall image using equations based on real processes. - Mixed models combine the advantages of previously described Models in the context of predictive modeling. 3) predictive models for different modes; The mixed models will combine some advantages of previously described models in the context of the predictive Modeling. The most used techniques used of these models are regression analysis between two or more important parameters for specific Water table. 4) Stages models have some advantages to classic models for the predictive Modeling

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 60 + 40 + 20 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

40 hours

16.2.

Independent tasks

60 hours

16.3.

Homework

20 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

40 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности 15, 16

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature