Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria
Bibliographic record
Abstract
The prediction of drought is of major importance in climate-related studies, hydrologic engineering, wildlife or agricultural studies. This study explores the ability of two machine learning methods to predict 1, 3, 6 and 12 months standardized precipitation and evapotranspiration index (SPEI) for the Wilsons Promontory station in Eastern Australia. The two methods are multiple linear regression (MLR) and artificial neural networks (ANN). The data-driven models were based on combinations of the input variables: mean precipitations, mean, maximum and minimum temperatures and evapotranspiration, for data between 1915 and 2012. Two performance metrics were used to compare the performance of the optimum MLR and ANN models: the coefficient of determination (R2) and the root mean square error (RMSE). It was found that ANN provided greater accuracy than MLR in forecasting the 1, 3, 6 and 12 months SPEI.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".