Outdoor Relative Humidity Prediction via Machine Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an environmental control system, relative humidity (RH) is a very important factor because of its direct impact on humans or even animals and plants. However, there are few studies focused on prediction of humidity variables. The main objective of this paper is to show the capability of machine learning algorithms for RH prediction. In this study, a Long-Short Term Memory (LSTM) and four popular machine learning algorithms; namely, Multi-Layer Perceptron (MLP), Random Forest (RF), k-Nearest Neighbor (KNN) and Support Vector Machine for Regression (SVMR) are presented for a year period of time-series relative humidity to predict for a particular case in an Italian city. In order to have precise performance, data pre-processing is done before running the models. This thorough examination proves the positive effect of all Machine Learning-based algorithms in time-series relative humidity prediction based on predictive accuracy. Over the different metrics, LSTM indicates the best performance among all considered algorithms.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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