S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting
Bibliographic record
Abstract
Abstract Droughts are prolonged periods of rainfall deficit, the frequency of which has increased due to global warming, causing severe impacts on water resources, agriculture, ecosystems, and food security. Given their significance, accurate monitoring and forecasting of droughts are crucial for effective water resource management. This paper introduces sequential-based transformers (S-Transformer), a novel deep-learning approach, aimed to apply for meteorological droughts prediction using their historical events. The core of the S-transformer algorithm is the orderly computing of an output by utilizing the sequence of inputs. Training of the S-transformer involves forward and backward passes through the network to adjust the weights and biases, using gradient descent optimization. This process uses fixed-size dynamic windows to minimize the difference between the observed and forecasted outputs. To demonstrate the effectiveness and performance of the new model, two case studies were presented based on the observed standardized precipitation index in Isparta and Burdur cities, Türkiye. In addition, the S-Transformer efficiency was compared with those of three benchmark models including a classic multilayer perceptron, a deep learning long-short-term memory, and a deep classic transformer model. The promising results of the proposed model proved its superiority over its counterparts in terms of different performance metrics. In Isparta and Burdur cities, the S-Transformer achieved the root mean squared values of 0.096 and 0.098 on the testing set, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".