S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it