Ensemble Machine Learning for Comprehensive Drought Assessment: A Case Study in the Mun Watershed of Northeast Thailand
Bibliographic record
Abstract
Drought indices are pivotal for comprehending and monitoring water scarcity in Northeast Thailand. Researchers precisely assess these indices, considering local climate conditions, geographical features, impacts on vegetation and agriculture, hydrological considerations, and the temporal and spatial scales of drought events. The reliability of these assessments depends on thorough validation against ground data, encompassing rainfall records and soil moisture measurements. This study explores the integration of various indices to enhance the overall comprehensiveness of drought assessments in the Mun watershed. It contributes to the field by evaluating drought conditions across the entire watershed, utilizing meteorological, soil moisture, and hydrological drought indicators. These indicators encompass the standardized runoff index, standardized precipitation index, standardized soil moisture index, and standardized precipitation evapotranspiration index. In order to establish a comprehensive multivariate drought index, this study employs ensemble learning, incorporating boosting techniques such as boosting, AdaBoost (adaptive boosting), and XGBoost (extreme gradient boosting). The performance of each model is assessed through comparisons using RMSE, MAE, and $$R^{2}$$ .
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".