Course detail

Artificial inteligence methods in water management

FAST-DSB026Acad. year: 2020/2021

Problems of uncertainty in rainfall-runoff modelling, stochastic processes, vague description of variables, adaptivity principle, learning systems, application of artificial neural networks, application of fuzzy models, application of genetic algorithms.

Department

Institute of Landscape Water Management (VHK)

Learning outcomes of the course unit

Student gains basic knowledge of using artifical inteligence methods in water management problems solution

Prerequisites

Hydrology, hydraulics, mathematics, probability theory and mathematical statistics, physics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. Problems of uncertainty in hydrology and water management.
2. Adaptivity principle and learning systems.
3.–4. Neural networks and their simulators.
5.–7. Application of neural networks on selected problems solutions.
8.–9. Fuzzy models.
10.–11. Application of fuzzy models.
12.–13. Genetic algorithms and their application.

Aims

Application of basic methods of artificial inteligence in hydrology and water management

Specification of controlled education, way of implementation and compensation for absences

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Classification of course in study plans

  • Programme DPC-V Doctoral, 2. year of study, winter semester, 8 credits, compulsory-optional
  • Programme DPA-V Doctoral, 2. year of study, winter semester, 8 credits, compulsory-optional
  • Programme DKA-V Doctoral, 2. year of study, winter semester, 8 credits, compulsory-optional
  • Programme DKC-V Doctoral, 2. year of study, winter semester, 8 credits, compulsory-optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer