An enhanced nonparametric streamflow disaggregation model with genetic algorithm
Bibliographic record
Abstract
Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance‐covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k‐nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season‐to‐season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".