A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction
Bibliographic record
Abstract
Abstract. This study proposes a novel hybrid method that substantially accelerates and improves deep learning (DL) model development for streamflow prediction. The method leverages a combination of a long short-term memory (LSTM) network and random forests. A hybrid encoder-decoder model is designed, where a pre-trained LSTM is utilized as an encoder to extract temporal features from the input data. Subsequently, the random forest decoder processes the encoded information to make streamflow predictions. Our method was tested on 421 catchments in the continental United States and 324 in Germany, both selected from two CAMELS datasets. The hybrid method has several benefits. First, it is much more efficient and robust than training LSTMs on each catchment individually (~14x faster). Second, it is much less computationally expensive than LSTM fine-tuning (i.e., feasible on a CPU-based workstation). Third, it achieves superior accuracy compared to a catchment-wise training strategy (e.g., 9.2 % improvement in the median in Nash-Sutcliffe Efficiency (NSE)), shows competitive performance compared to regional LSTM models when trained with fewer data, and through fine-tuning, improves regional LSTM performance in out-of-training samples by 13.13 % (median NSE). To our knowledge, this is the first decision-tree model integrated within a DL workflow to enhance fine-tuning efficiency of pre-trained models in new locations. This hybrid approach holds significant promise for future applications in hydrological modeling, particularly considering the imminent rise of geospatial foundation models in hydrology that will rely on transfer learning techniques for effective deployment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".