ENSEMBLE ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF DEW POINT TEMPERATURE
Why this work is in the frame
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Bibliographic record
Abstract
Dew point temperature is needed as an input to calculate various meteorological variables. In general, it contributes to human and animal comfort levels. The goal of this study was to develop artificial neural network (ANN) models for dew point temperature prediction to improve upon previous research. These improvements included optimizing the stopping criteria, comparing seasonal models to year-round models, and developing ensemble ANNs to blend the output of seasonal models. For an ANN trained with 100,000 patterns per epoch, the error was reduced using a 2000-pattern stopping dataset at an interval of 20 learning events to decide when to stop training. Seasonal ANN models were blended in an ensemble ANN with the weight of the member networks determined using a fuzzy membership-type function based on the day of year. These ensemble models were shown to produce lower errors than year-round, nonensemble models. The mean absolute errors (MAEs) of the final models evaluated with an independent evaluation dataset included 0.795°C for a 2-hour prediction, 1.485°C for a 6-hour prediction, and 2.146°C for a 12-hour prediction. The final model MAEs, when compared to the previous research, were reduced by 0.008°C, 0.081°C, and 0.135°C, respectively. It can be concluded that the methods used in this research were effective in more accurately predicting year-round dew point temperature. The ANN models for different prediction periods were sequenced to provide a 12-hour dew point temperature prediction system for implementation on the Georgia Automated Environmental Monitoring Network website (www.georgiaweather.net).
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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 | 0.000 |
| 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 it