Forecast Rainfall Density by Utilizing Machine Learning Models
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
Organizations can use weather forecasting to help with decision-making when it comes to preventing disasters.Forecasting rain is challenging since weather conditions are always unpredictable in general.The prediction of rainfall uses a variety of methodologies, including statistical, hybrid, and physical approaches.In this research, we have implemented various machine learning models such as Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP) to predict the density of rainfall.This study has used Taiwan Ruiyan rainfall hourly dataset from 1998 to 2018 which contains five features like Air Pressure, Humidity, Temperature, Windspeed, and Wind Direction to predict the rainfall density such as low, medium, and heavy rainfall.The results data in this study are compared using statistical metrics like AUC, accuracy, recall, precision, and F1-score.The Random Forest, and Multi-Layer Perceptron models, had the highest accuracy scores of 0.71, accurately predicting the results.This study offers a comprehensive overview of several methods and their rainfall density predictions.By comparing these models, we can decide which one is best for predicting rainfall.The suggested work is extensively used in a variety of agriculture and civil applications, including hazard prediction, prevention, operational planning, and many more.
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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 | 0.001 |
| 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