Application of meteorological, hydrological and remote sensing data to develop a hybrid index for drought assessment
Bibliographic record
Abstract
In this study, a hybrid index called the aggregated drought index (ADI) is developed for drought assessment integrating various meteorological, hydrological, and agricultural features of the region. To this end, precipitation, reservoir storage, discharge, temperature, potential evapotranspiration, and the degree of vegetation based on remote sensing images are utilized to quantify ADI. Then, the performance of two models, auto-regressive integrated moving average (ARIMA) and adaptive neuro-fuzzy inference system (ANFIS), is investigated in predicting the developed drought index. The proposed framework is applied to the Aharchay watershed located in East Azarbaijan Province, Iran. The results demonstrate that the region has experienced normal and mild drought conditions during the investigated time period. ADI is also applied in another watershed (Ajichay) to show ADI’s capability in different climatic settings. Regarding the predictive capability, the ANFIS model outperforms ARIMA in drought prediction, particularly in severe weather conditions. The ADI benefits planning for drought mitigation and preparedness by incorporating several different aspects of the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".