The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/9a6a0464-2981-470a-8d7a-48c7e7fff27d

Koo, Ja-Ho; Edo Abraham; Jonoski, Andreja; Solomatine, Dimitri (2025): Code underlying: Balancing Operator’s Risk Averseness in Model Predictive Control for Real-time Reservoir Flood Control. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/9a6a0464-2981-470a-8d7a-48c7e7fff27d.v3

Version 2 - 2025-03-25 Version 1 - 2025-02-05

Categories

• Civil Engineering
• Land and Water Management
• Engineering
• Environment

Keywords

Dynamic Weight Flood control Model Predictive Control MPC Reservoirs

Geolocation

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

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by Ja-Ho Koo , Edo Abraham, Andreja Jonoski , Dimitri Solomatine

Python codes for the parameterized dynamic MPC implementation.

Inflow data is collected from the Data portal managed by Korean government.

PDMPC main is the python file to run the PDMPC, and other files are for the function or class using in main.py.

Formulation.py contains only linear MPC formulation by Pyomo. Evaluator.py includes python functions to evaluate the linear MPC results.

History

• 2025-02-05 first online

• 2025-05-28 published, posted

Publisher

4TU.ResearchData

Format

.txt, .zip, .py

Associated peer-reviewed publication

Balancing Operator’s Risk Averseness in Model Predictive Control for Real-time Reservoir Flood Control.

Organizations

IHE Delft, Department of Hydroinformatics and Socio-Technical Innovation
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management
Korea Water Resources Public Corporation (K-water)