Predictive Modeling of Hourly Air Temperature Based on Atmospheric Conditions of Karak in Jordan
Bibliographic record
Abstract
In this paper, the focus is mainly on building machine learning (ML) models for AAT forecasting every hour over Karak City in Jordan.The dataset consisted of comprehensive meteorological readings, which were subject to heavy preprocessing in order to establish data integrity essential for building strong ML models.The investigation involved a number of ML models Support Vector Regression (SVR) with RBF Kernel, Decision Tree Regressor (DTR), Ridge Regressor (RR) & Lasso Regressors (LSR) and Linear Regression (LR) because each was found to have unique strengths in capturing the intricate dynamics of temperature behavior.Excellent accuracy of the models, mainly SVR with RBF Kernel and relevance for better forecasting of weather in a region with peculiar difficulties to data-based modeling were shown by it.Our research not only confirmed the capability of different ML methodologies in regional temperature forecasting but also provided an important reference for planners and stakeholders concerned with environmental planning and management.The study provides a better understanding of the regional climate adaptation approaches, vide its case location in Karak City only whereas support local data analysis is necessary to address global climate variability.The results have important implications for the improvement of decision-making in agriculture, disaster management and sustainability schemes especially under changing climatic conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".