Long Short-Term Memory Model to Forecast River Ice Breakup Throughout Alaska USA
Bibliographic record
Abstract
Predicting the timing of annual river ice breakup is crucial for residents to prepare for potential flooding and assess the safety of rivers for transportation. This analysis develops a deep learning approach using meteorological and geospatial data products to forecast river ice breakup. We selected 33 locations along eight major rivers across Alaska, USA, and Western Canada, leveraging annual breakup dates from the Alaska-Pacific River Forecast Center database. Daily meteorological data from Daymet, along with static watershed attributes from the pan-Arctic catchment database, were used to develop a Long Short-Term Memory model (LSTM) for predicting river ice breakup. Of the 33 locations, 23 were used for training the LSTM. The model demonstrated high efficacy, accurately predicting the annual breakup date with a mean absolute error (MAE) of 5.40 days, a standard deviation of 4.03 days and a mean absolute percentage error (MAPE) of 4.37%. The spatial generalizability of the LSTM was evaluated using the remaining 10 locations as holdouts, with eight of the 10 locations averaging a MAPE of less than 8% over the entire time series (1980 to 2023). Additionally, we retrieved 51 long-range seasonal forecast ensembles from the Copernicus Climate Data Store and applied trained LSTM to them to showcase the capability of the LSTM to predict future river ice breakup using operational weather forecasts. To analyze marginal contribution of LSTM inputs for predictions, Shapley values were calculated. A new temporal correction scheme was developed and applied to Shapley values to address unique features of the breakup data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".