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- Title
Prognostication of Weather Patterns using Meteorological Data and ML Techniques.
- Authors
Mathur, Saksham; Kumar, Sanjeev; Choudhury, Tanupriya
- Abstract
In the field of modern weather prediction, the accurate classification is essential, impacting critical sectors such as agriculture, aviation, and water resource management. This research presents a weather forecasting model employing two influential classifiers random forest and technique based on gradient boosting, both implemented using the Scikit-learn library. Evaluation is based on key metrics including F1 score, accuracy, recall, and precision, with Gradient Boosting emerging as the superior choice for precipitation prediction. The study examines the performance of Random Forest Regression, Gradient Boosting Regression, and Radial Basis Function Neural Network in forecasting precipitation, drawing on prior research that demonstrated the superiority of the Random Forest algorithm in terms of accuracy and speed. Ensemble methods, particularly the Voting Classifier, a fusion of Random Forest and Gradient Boosting, outperform individual models, offering a promising avenue for advancing weather classification.
- Subjects
METEOROLOGY; RADIAL basis functions; ARTIFICIAL neural networks; RANDOM forest algorithms; WEATHER forecasting; WATER supply management
- Publication
EAI Endorsed Transactions on the Energy Web, 2024, Vol 11, Issue 1, p1
- ISSN
2032-944X
- Publication type
Academic Journal
- DOI
10.4108/ew.5648