A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting
Why this work is in the frame
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Bibliographic record
Abstract
The precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins. Achieving this objective necessitates the development of highly accurate river water level forecasts. Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge-Kutta optimization (RUN) algorithm for parameter optimization. The L-SKRidge model combines the advantages of both the SKRidge and ridge regression techniques, resulting in a more robust and accurate forecasting tool. By incorporating the linear relationship and regularization techniques of ridge regression with the flexibility and adaptability of the SKRidge algorithm, the L-SKRidge model is able to capture complex patterns in the data while also preventing overfitting. The L-SKRidge method is applied to forecast water levels in the Brook and Dunk Rivers in Canada for two distinct time horizons, specifically one- and three days ahead. Statistical criteria and data visualization tools indicates that the L-SKRidge model has superior efficiency in both the Brook (achieving R = 0.970 and RMSE = 0.051) and Dunk (with R = 0.958 and RMSE = 0.039) Rivers, surpassing the performance of other hybrid and standalone frameworks. The results show that the L-SKRidge method has an acceptable ability to provide accurate water level predictions. This capability can be of significant use to academics and policymakers as they develop innovative approaches for hydraulic control and advance sustainable water resource management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it