Improving explainable AI in attributing hydrological responses to climate variabilities in snow-dominated watersheds
Bibliographic record
Abstract
Explaining the decision-making of machine learning (ML) models, known as interpretation, connects data-driven results to real-world hydrological processes, representing the next major challenge in ML applications for attribution, beyond accurate simulation. To improve ML interpretability in watershed-scale hydrological attribution, this study develops an eXplainable Artificial Intelligence (XAI) framework that incorporates a novel interpretation algorithm, Lagrange Multipliers-Support Vectors (L-SV), within a feature-based, multi-criteria constraint ML framework termed Climate Feature-Bootstrapped Support Vector Regression (CF-BootSVR). SVR simulations have been conducted in two snow-dominated watersheds, showing satisfactory simulation accuracy (average R² and NSE ≥ 0.88). The aggregated features enhance model interpretability with physically meaningful inputs and reduce computational costs by up to 30 times. The multi-criteria-layer design improves robustness and generalizability (declines in R² and NSE ≤ 0.11) while reducing uncertainties compared to single-run models. L-SV ranks feature importance similarly to model-agnostic algorithms, Permutation Feature Importance (Perm) and SHapley Additive exPlanations (SHAP), particularly in identifying the most sensitive features. L-SV also provides additional directions for feature contribution and enhances computational efficiency, being over 2513 and 2023 times faster than SHAP in the watersheds of Greata and 240, respectively. Furthermore, from a physical-based perspective, the XAI-derived attributions align with general hydrological expertise. Consequently, we conclude that CF-BootSVR offers an efficient approach to enhance predictive capabilities and deepen our understanding of climate-runoff relationships. Beyond hydrology, this CF-BootSVR framework establishes a generalizable paradigm for addressing issues related to climate seasonality. Moreover, L-SV demonstrates significant potential for broader applications in interpreting SVR models across diverse research domains.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".