Improving Forecasts of Nile Flood Using SST Inputs in TFN Model
Bibliographic record
Abstract
Egypt depends on the Nile River for all of its water resources. Using the streamflows' history, the large fluctuations of the Nile flood cause the best predictions to be unsatisfactory. The purpose of this paper is to stochastically forecast the Nile summer runoff one-season-ahead using, as inputs, an El Niño-southern oscillation (ENSO) sea surface temperatures (SSTs) signal in the East Pacific and SSTs in the South Indian Ocean. Causality between inputs and outputs is established, and a multiple-input transfer function with noise (TFN) model is built for forecasting purposes. The model explains 63% of the variability of the Nile flood with relatively stable parameters. The mean of absolute percentage error of forecasts is 6% calculated on a data set that was not used in the parameter estimation. The model is parsimonious, and its behavior agrees with the most recent studies in climatology. The forecasting ability of the model is high for extreme floods and severe drought years, except when the South Atlantic Ocean displays a strong warm signal opposite to the El Niño-southern oscillation cold signal.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".