Rainfall forecasting in arid regions using an ensemble of artificial neural networks
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 Water rainfall prediction is one of the most difficult tasks in hydrology because rainfall events are extremely random. This research presents a comparative analysis of different models that predict rainfall in an arid region. The forecasting models comprise the feed-forward, general regression, recurrent, cascade, and Elman neural networks. The performance of the aforementioned models is assessed using three evaluation metrics, namely the correlation coefficient, coefficient of efficiency, and Willmott’s index of agreement. Furthermore, the statistical significance of the neural network models is evaluated using the Wilcoxon-Mann-Whitney test. Finally, the correspondence of the neural network model results compared to the observations is examined using the Taylor diagram. The findings reveal that the general neural network exhibits the best performance compared to other models using the tropical rainfall measuring mission dataset at Suez city in Egypt. The Egyptian water municipality is intended to benefit from the proposed model in monthly rainfall forecasting in this arid region. The precise modeling of rainfall is vital for managing water resources such as food production, water allocation, and drought management.
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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 | 0.001 |
| 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 it