Integrating Machine Learning in IoT Solutions for Real-Time Weather Forecasting Systems
Bibliographic record
Abstract
In recent years, the role of the Internet of Things (IoT) in monitoring and predicting various environmental phenomena has expanded significantly.This study presents the design and implementation of an intelligent IoT ecosystem tailored for weather monitoring stations.The core objective of this system is to enhance the accuracy and responsiveness of weather forecasting by integrating machine learning (ML) techniques.This scalable IoT ecosystem efficiently collects comprehensive meteorological data from all sensors, such as temperature, humidity, and atmospheric pressure, using an ESP32 as a microcontroller.This combination of specialized hardware and advanced software techniques markedly boosts prediction accuracy, presenting a pioneering step in environmental monitoring methodologies.These algorithms are well-known for their adaptive learning capabilities and dynamically update predictions based on real-time and historical datasets.With the strategic inclusion of cloud computing, data accessibility and scalability have been remarkably enhanced.This amalgamation of specialized hardware, intelligent software, and cloud infrastructure significantly amplifies prediction accuracy, heralding a new era in environmental monitoring methodologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".