A novel decomposition-based localized short-term tidal current speed and direction prediction model
Bibliographic record
Abstract
To integrate tidal energy into the existing grid, it is important to accurately predict the amount of tidal energy available in the short-term horizon. In this regard, the short-term prediction of tidal current becomes crucial The tidal energy depends not only on the tidal current speed (TCS), but also on the tidal current direction (TCD). The non-stationarity and non-linearity of the TCS and TCD time series lower the predictability of them. Using decomposition approaches, these non-linear and non-stationary time series can be decomposed into several components which are more predictable. On the other hand, in order to predict any volatility of a non-linear time series, localized approaches perform better compared to training the prediction model in a global fashion. In this regard, this paper proposes a novel prediction model based on ensemble empirical mode decomposition (EEMD) and localized least squares support vector machine (LSSVM) to increase the prediction accuracy of TCS and TCD. The proposed model is compared with the auto-regressive integrated moving average (ARIMA) model and the LSSVM-based model. The actual data recorded from the Shark River Entrance (Florida, U.S.) is used to verify the applicability of the proposed prediction model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".