Applying Deep Learning to Predict Adverse Environmental Conditions in Fish Aquaculture Pens
Bibliographic record
Abstract
Aquaculture is critical for meeting the increasing global seafood demand as wild fish stocks decline. Effective management of aquaculture environments is vital for sustainability and operational efficiency. This study applies advanced deep learning models, specifically Temporal Fusion Transformers (TFT), to predict dissolved oxygen (DO) levels in fish pens. Accurate DO forecasting is essential for optimizing feeding schedules, enhancing fish health, and reducing environmental impact. Using extensive datasets from aquaculture sites in Canada and Scotland, our models were trained and validated to deliver reliable 24-hour DO predictions. Our phased approach, starting with single-pen data and scaling to multiple pens across various farms and regions, achieved mean absolute error (MAE) values below 0.7 for 24-hour forecasts. The findings demonstrate the potential of deep learning models in assisting real-time decision-making and mitigation strategies in aquaculture, fostering sustainable and efficient practices. Future work could incorporate additional environmental variables or farm inputs and extend model applications to diverse aquaculture systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".