Learning Basin Similarity Through Combined Deep Learning and Random Forest Approaches for Improved Parameter Transfer in Ungauged Basins
Bibliographic record
Abstract
Parameter regionalization for ungauged basins remains a critical challenge in hydrological modeling. While traditional approaches rely on physical catchment descriptors or spatial proximity, and recent machine learning applications have focused primarily on direct streamflow prediction, there remains significant potential to leverage machine learning for improved parameter transfer strategies. This study explores novel approaches that combine Long Short-Term Memory (LSTM) networks and Random Forest (RF) models to predict basin similarity and optimize parameter transfer for physically-based hydrologic models. Using case studies from British Columbia's Fraser River Basin and Ontario's Great Lakes region, we test multiple methodologies for integrating deep learning with traditional parameter transfer approaches. Our primary benchmark is established through an exhaustive parameter transfer experiment using the Raven hydrological model, where parameters from each potential donor basin were transferred to every possible receiver basin across 10 independent trials. This benchmark represents the best achievable KGE via parameter transfer methods. Our framework employs a regional LSTM model to capture complex streamflow dynamics and characterize basin similarity, then explores various RF-based approaches to predict optimal donor-receiver basin pairs for parameter transfer. These methods are evaluated against both the exhaustive transfer benchmark and emerging machine learning approaches. Results indicate that thoughtfully combining deep learning and random forest techniques can capture nuanced relationships between basin characteristics and hydrological response similarity, advancing the state-of-the-art in parameter regionalization for ungauged basins while maintaining physical interpretability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".