Hydroinformatics
| Title | Υδροπληροφορική / Hydroinformatics |
| Code | ΝΔΕΕΠ110 |
| Faculty | Agriculture, Forestry and Natural Environment |
| School | Agriculture |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Pantazis Georgiou |
| Common | No |
| Status | Active |
| Course ID | 600026093 |
Programme of Study: 2024- sīmera
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 7 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600290389
|
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600290389
- eLearning (Moodle):
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Upon completion of this course, students will be able to:
1. apply statistical methods for analysis of hydrological variables
2. apply stochastic models and artificila intelligent models for water resources management
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Respect natural environment
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Analysis, simulation and synthetic generation of hydrologic time series. Geostatistics. Periodogram and spectral analysis of hydrologic data. Models for hydrologic time series analysis. Non-seasonal and seasonal autoregressive integrated moving average models. Transfer function-noise models. Intervention analysis models. Optimization methods of water resources systems. Metahereustic algorithms, multi-criteria analysis and neural networks. Decision support systems - estimation of uncertainty. Models and applications in PCs.
Keywords
statistical analysis, ARIMA models, artificila intelligent models
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Description
Teaching: Powerpoint.
e-class platform: e-learning.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 117 | ||||
| Project | 76 | ||||
| Exams | 3 | ||||
| Total | 196 |
Student Assessment
Description
70% written exams
30% project
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Additional bibliography for study
Kumar, P., Folk, M., Markus, M., & Alameda, J. C. (2005). Hydroinformatics: data integrative approaches in computation, analysis, and modeling. CRC Press.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Simonovic, S. P. (2012). Managing water resources: methods and tools for a systems approach. Routledge.
Last Update
21-01-2025
