Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments
Bibliographic record
Abstract
Abstract Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of $$\textrm{R}^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mtext>R</mml:mtext> <mml:mn>2</mml:mn> </mml:msup> </mml:math> and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".