An advanced multistage multi-step tidal current speed and direction prediction model
Bibliographic record
Abstract
Non-stationarity and non-linearity of the tidal current speed (TCS) and tidal current direction (TCD) time series are among the main barriers for enhancing the TCS and TCD prediction accuracy. In this regard, this paper proposes an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) which is employed to decompose the non-stationary TCS and TCD time series into several components (modes) with unique characteristics. Then, to capture the nonlinear pattern of TCS and TCD in different modes, several prediction engines based on least squares support vector machine (LSSVM) are developed. To modify the prediction error which occurs in predicting different components, a prediction modification stage based on a combination of extreme learning machines (ELMs) is utilized to reconstruct the final prediction values. The proposed TCS and TCD prediction model, named ICEEMDAN-LSSVM-ELM, has been evaluated using the data recorded from Shark river entrance, NJ. Performance of the proposed prediction model is compared with various well-developed benchmark models.
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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 | 0.001 |
| 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 | 0.000 | 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".