Multi-step ahead significant wave height forecasting using global and local view graph representation based on GRU model
Bibliographic record
Abstract
Significant wave heights (SWH) are important to be predicted accurately for clean wave energy production, and beach erosion risks. The existing models lack the ability to analyse the dynamic behaviour of oceanic drivers, as a result, they cannot predict SWH at different forecasting horizons. In this paper, an innovative modelling scheme (termed as GLG-DL) based on graph deep learning which integrated global and local graph features has been designed to predict SWH. The global and local graph leaners enable GLG-DL model to capture the global information, and the local trends from oceanic drivers. The extracted graph representations are then used into the gated recurrent unit (GRU) based encoder and decoder to predict multistep ahead SWH for Palm Beach, Gladstone, and Albatross Bay stations, Australia. The GLG-DL model was compared with Auto-regression model (ARM), Auto-regressive multilayer perceptron (AR-MLP), Recurrent neural network (RNN), RNN based attention mechanism (RNN-AM), RNN based Long Short-term Memory (RNN-LSM), Spatial-temporal attention mechanism (STAM), and improved recurrent neural networks (SNN) models. The results demonstrated that the GLG-DL attained higher performance to forecast multistep ahead SWH for all stations. The GLG-DL model is beneficial in the application and optimization of clean energy resource generations worldwide.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".