A Systematic Analysis of Meteorological Parameters in Predicting Rainfall Events
Bibliographic record
Abstract
Accurate rainfall prediction is of paramount significance across diverse sectors, particularly in agriculture, where accurate predictions play a pivotal role in effective resource management and decision-making. However, due to the complexity and dynamic structure of climate systems, rainfall prediction is a difficult task. This study shifts the focus towards exploring correlations and feature selection in the context of rainfall prediction, contributing to a more sophisticated understanding of the process. By analyzing five years of weather data from three weather stations in the United States, Canada, and Ireland, the study delves into the interactions between meteorological features and rainfall occurrences. The use of a machine learning (ML)-based feature importance technique, which enables the identification of key meteorological features that significantly contribute to rainfall prediction, is central to the work. As a result, this method improves understanding of meteorological conditions, which act as accurate forecasters of rainfall outcomes and can help to develop accurate decision-support systems. The study also conducts a thorough assessment of prediction performance of various ML and deep learning (DL) techniques such as Classification and Regression Trees (CART), Support Vector Machine (SVM) and Dense Neural Networks (DNN).The findings show that the models using only the important meteorological features in the dataset perform better than using all the features. This rigorous examination also supports the selection of appropriate rainfall forecast models for specific use cases. Overall, this study increases our understanding of rainfall prediction by focusing on the investigation of correlations between meteorological indicators and the identification of key meteorological features using ML approaches, offering valuable insights for weather forecasting applications. This nuanced analysis contributes to the advancement of predictive modeling in the realm of rainfall forecasting, offering potential implications for decision-making across sectors reliant on precise weather forecasts.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".