Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks
Bibliographic record
Abstract
Extreme rainfall in Queensland during December 2010 and January 2011 resulted in catastrophic flooding, causing loss of life, extensive property damage and major disruption of economic activity.Official medium-term rainfall forecasts failed to warn of the impending heavy rainfall.Since the flooding, the Australian Bureau of Meteorology has changed its method of forecast from an empirical statistical scheme to the application of a general circulation model (GCM), the Predictive Ocean and Atmospheric Model for Australia (POAMA).Our previous studies demonstrated that more skilful monthly rainfall forecasts can be achieved using artificial neural networks (ANNs).This study extends those previous investigations focussing on the capacity of the forecast methodology to differentiate between extreme rainfall events and more average conditions, up to one year in advance.Sites within two geographical regions of Queensland are examined: (i) coastal Queensland using rainfall observations from Bingera, Plane Creek and Victoria Mill; (ii) a region of south-east Queensland, using rainfall observations from 54 weather stations, extending approximately 300 km northward along the Queensland coast, from the Gold Coast to Bundaberg, and approximately 200 km inland.For both regions, the capacity to differentiate between average conditions and impending extreme rainfall events up to one year in advance is demonstrated.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".