Codes underlying: Stochastic Model Predictive Control with Conditional Value-at-Risk Constraints for Short-term Reservoir Flood Control

DOI:10.4121/dc6b16b1-aab9-4ddb-a96b-8289ae3c8d5b.v1
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/dc6b16b1-aab9-4ddb-a96b-8289ae3c8d5b

Datacite citation style

Koo, Ja-Ho; Edo Abraham; Jonoski, Andreja; Solomatine, Dimitri (2025): Codes underlying: Stochastic Model Predictive Control with Conditional Value-at-Risk Constraints for Short-term Reservoir Flood Control. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/dc6b16b1-aab9-4ddb-a96b-8289ae3c8d5b.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Geolocation

Upstream and downstream area around Daecheong multi-purpose reservoir in South Korea
lat (N): 36.4775
lon (E): 127.480833
view on openstreetmap

Time coverage

2011-2020

Licence

CC BY 4.0

Interoperability

The dataset and codes for the paper "Stochastic Model Predictive Control with Conditional Value-at-Risk Constraints for Short-term Reservoir Flood Control."

Including reservoir inflow and downstream water level observation data for the Daecheong reservoir in South Korea, there are codes to implement a stochastic MPC and deterministic MPC experiments.

History

  • 2025-05-28 first online, published, posted

Publisher

4TU.ResearchData

Format

.py; .txt

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)

DATA

Files (7)