Hybridizing persistent homology and machine learning for oceanographic time series
Bibliographic record
Abstract
ABSTRACT Oceanographic phenomena are inherently complex due to factors such as global warming, ice melting, and human activities. Analyzing oceanographic data presents a challenging task, and a quantitative approach is often implemented to address these complexities. Traditional machine learning techniques, such as regression and cluster analysis, are commonly employed for predicting and identifying similarities in oceanographic conditions across different locations. This study explores the integration of qualitative approaches into oceanographic data analysis by developing hybrid models that combine topological data analysis (TDA) with traditional machine learning methods. The focus is on extracting qualitative features using persistent homology, the core tool of TDA, and incorporating these features into machine learning models such as support vector regression (SVR) and hierarchical clustering analysis (HCA). The study applies these methods to the following two oceanographic datasets: wave height data from buoy stations in Canada and sea level data from gauge stations in Japan. The results show that the hybrid models significantly outperform traditional approaches across several performance metrics, including the R2 score, maximum absolute error, mean squared error, and Rand score. This demonstrates the value of adding topological information to machine learning models, which enhances their predictive performance.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".