Using artificial intelligence to forecast monthly rainfall under present and future climates for the bowen basin, Queensland, Australia
Bibliographic record
Abstract
There is a need for more skilful medium-term rainfall forecasts for the Bowen Basin, a key coal-mining region in Queensland, Australia.Prolonged heavy rainfall during the 2010-2011 summer was not forecasted and it severely affected industry operations.Official forecasts are currently based on general circulation models (GCMs) and indicate there will be change in the timing and strength of the rainfall in the Bowen Basin with climate change.A more skilful medium-term rainfall forecast for the present climate can be achieved through the use of artificial neural networks (ANNs).ANN can be used to generate monthly forecasts 3 months in advance.These forecasts can be improved through a weighted linear combination of forecasts.Principal component analysis prior to inputting data does not improve the forecast.An ANN can provide an independent method of GCM validation under future climates with results in reasonable agreement with the averaged values from the GCM ensembles: suggesting a decline in summer rainfall and an increase in winter rainfall at Nebo, a locality in the Bowen Basin, under the 3°C warmer scenario.This represents a smoothing of the annual variability in rainfall for the locality of Nebo rather than more climatic extremes with global warming.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".