The course is designed for engineers and scientists involved in operational water management and control and interested to broaden their knowledge of modern approaches and modelling tools. The course could be also interesting to PhD and Master students conducting research in real-time control of water systems and/or use of data-driven models in hydrology, hydraulics or environment. Pre-requisites are a basic knowledge of mathematics, hydrology and systems analysis.
PrerequisitesBasic knowledge of statistics, hydrology and hydraulics.
Dates, Fee, ECTS
Start: 02 March 2020
End: 20 March 2020
ECTS credit points: 5
Deadline IHE application: 01 February 2020 - 23.59 (CET)
Course fee: € 2910
VAT is not included in the course fee
- Understand the principles and techniques of optimisation, and formulate and solve optimisation problems related to modelling and water management
- Understand and apply the principles and techniques of real-time control, and anticipatory water management
- Understand the main principles of data assimilation using Kalman filter and related techniques
- Understand and apply data-driven modelling using computational intelligence techniques (neural networks, model trees, instance-based learning), and select proper methods and tools
The course includes three main parts:
1. Introduction to optimisation.
Classical optimisation. Linear and non-linear optimisation. Derivative-based and direct methods. Dynamic programming. Global (multi-extremum) optimisation. Genetic and evolutionary approaches. Multi-objective optimization. Applications in water sector. Exercise: optimal water allocation; automatic model calibration.
2. Real time control of water systems.
Introduction to Real-Time Control. Modelling hydrological systems and optimal control problems with AQUARIUS and SWMM. Control-systems functions and techniques. Hardware and software components. Control systems in industry. Identifying control system components. Use of Kalman filters. One day field trip to North-West Netherlands.
3. Data driven modelling and computational intelligence.
Modelling in the framework of Hydroinformatics. Data-driven and physically based models. Overview of machine learning and computational intelligence. Decision, regression and M5 model trees. Artificial neural networks. MLP and RBF networks. Instance-based learning. Fuzzy logic and fuzzy rule-based systems. Hybrid models combining simulation and data-driven models. Error correction and data assimilation techniques.
Exercises: using data driven methods in hydrological forecasting.