Comparing single and multi-objective hydrologic model calibration considering reservoir inflow and streamflow observations
Bibliographic record
Abstract
Calibration techniques were investigated on how to best optimize a 149 parameter, distributed hydrological model of the Lake of the Woods – Rainy Lake (LOWRL) watershed. Single objective calibrations based only on streamflow, only on reservoir inflows or average performance of both observation types, optimized using the Dynamically Dimensioned Search (DDS) algorithm, were compared with a multi-objective optimization approach with both observation types using the Pareto Archived DDS (PADDS) algorithm. Results from synthetic calibration tests against a known solution showed that PADDS was able to repeatedly find solutions with streamflow and reservoir inflow Nash-Sutcliffe coefficients of more than 0.95 and 0.99 using 2000 and 8000 model evaluations, respectively, demonstrating the effectiveness of PADDS on a limited calibration budget. When the LOWRL model was calibrated to actual observations with PADDS using 2000 evaluations, the algorithm repeatedly returned solutions with validation period streamflow and reservoir inflow Nash-Sutcliffe coefficients of approximately 0.71 and 0.87, respectively. Results demonstrate the capabilities of PADDS to reasonably calibrate a large dimensional hydrologic model on a restricted budget of 2000 model evaluations and highlight the importance of calibrating to both reservoir inflows and streamflows simultaneously. Considering the comparative results under multiple calibration trials, the multi-objective formulation solved by PADDS is shown to generate equivalent quality results as a weighted single objective approach solved by DDS (averaging reservoir inflow and streamflow calibration objectives).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".