TY - DATA
T1 - Codes underlying: Comparison of scenario reduction approaches for reservoir inflow scenarios generated by a Bayesian Neural Network
PY - 2025/03/25
AU - Ja-Ho Koo
AU - Edo Abraham
AU - Andreja Jonoski
AU - Dimitri Solomatine
UR - 
DO - 10.4121/e343331b-496f-40ab-83eb-f546df6dffa6.v1
KW - Wasserstein distance
KW - energy distance
KW - Euclidian distance
KW - Manhattan distance
KW - scenario reduction
KW - BNN
KW - scenario generation
N2 - <p>The data set and codes for a paper, Comparison of scenario reduction approaches for reservoir inflow scenarios generated by a Bayesian Neural Network.</p><p>Including reservoir inflow data for the Daecheong reservoir in South Korea, there are codes to build a BNN model with hyperparameter optimization using the TPE algorithm. In addition, codes for scenario reduction by four different measures, Wasserstein, energy, Euclidean, and Manhattan distances, are integrated.</p>
ER -