Stochastic Hydrology and Hydroinformatics

Course Information
TitleΣΤΟΧΑΣΤΙΚΗ ΥΔΡΟΛΟΓΙΑ ΚΑΙ ΥΔΡΟΠΛΗΡΟΦΟΡΙΚΗ / Stochastic Hydrology and Hydroinformatics
FacultyAgriculture, Forestry and Natural Environment
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
Course ID420000897

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
Course Type 2016-2020
  • Scientific Area
  • Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Examination)
Required Courses
  • ΜΥΝ726 Water Resources Management
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
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Descriptive statistics. Probabilities and random variables. Statistical analysis of extreme values. Regression analysis in hydrology. Characteristics of hydrologic time series (homogeneity, stationary, ergodicity, trend, periodicity, persistence). 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. Kalman filters. Artificial intelligent models (neural networks, genetic algorithms etc), decision support models and risk estimation (fuzzy logic etc) in water resources.
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 Laboratory Teaching
  • Use of ICT in Communication with Students
Teaching: Powerpoint. e-class platform: moodle. Laboratory exercises.
Course Organization
Written assigments
Student Assessment
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)
  • Written Exam with Problem Solving (Formative, Summative)
Additional bibliography for study
Παπαμιχαήλ, Δ.Μ., 2004. Τεχνική Υδρολογία Επιφανειακών Υδάτων. Εκδόσεις Γιαχούδη. Παπαμιχαήλ, Δ.Μ. και Γεωργίου, Π.Ε., 2011. Στατιστική Υδρολογία. Σημειώσεις για τη μεταπτυχιακή ειδίκευση «Γεωργικής Μηχανικής και Υδατικών Πόρων», Θεσσαλονίκη, σελ. 113. Παπαμιχαήλ, Δ.Μ. και Γεωργίου, Π.Ε., 2011. Στοχαστική Υδρολογία. Σημειώσεις για τη μεταπτυχιακή ειδίκευση «Γεωργικής Μηχανικής και Υδατικών Πόρων», Θεσσαλονίκη, σελ. 208.
Last Update