Development of a hydrometeorological drought severity composite index based on the integration of multisource characteristics and an explainable artificial intelligence model
Bibliographic record
Abstract
The Oum Er Rbia watershed, Morocco, is a region facing severe water stress conditions associated with the simultaneous occurrence of meteorological and hydrological droughts. This study proposes a new hydrometeorological drought composite index (HDCI) by adapting an explainable artificial intelligence (XAI) approach for synergistic integration of multisource drought-related indicators. The streamflow anomalies, hydroclimatic coefficients and water variations in dams were comparatively explored as response variables for the selection and weighting of the HDCI components using Shapley additive explanations theory (SHAP). The severity of hydrometeorological drought in Oum Er Rbia watershed is regulated by the interaction of several factors, among which the contribution of terrestrial water storage to hydrometeorological drought related to streamflow anomalies tends to become more pronounced as the number of influencing factors decreases. The new composite index is highly correlated with the reference hydrometric variables. However, heteroscedasticity between hydrometric stations influences the performance of the HDCI. Therefore, the integration of factors based on spatial dependencies represents a potential avenue for reducing the influence of spatial heterogeneities. Overall, by integrating exclusively geospatial and reanalysis data, HDCI has advantages for the assessment of hydrometeorological drought conditions at the pixel scale compared with conventional methods, which use direct measurements of hydrometric variables but are often discontinuous and unavailable in real time. • XAI approach proved particularly effective in identifying and weighting relevant features. • The new HDCI index reflects various dimensions of hydrometeorological drought severity. • Progressive integration of the driving factors has enabled us to identify the optimum threshold. • Adding factors does not improve HDCI performance above the saturation limit. • The HDCI was strongly correlated with hydrometric measurements, particularly in the upper watershed.
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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 | 0.000 |
| 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".