Prognostication of Weather Patterns using Meteorological Data and ML Techniques

• DOI: 10.4108/ew.5648
• Publication Date: 2024-04-05T07:54:32Z
• Authors: Saksham Mathur; Sanjeev Kumar; Tanupriya Choudhury
• Keywords: Science; Q; Classifier Random Forest; QA75.5-76.95; Gradient Boosting; Precipitation Prediction; Radial Basis Function Neural Network; Electronic computers. Computer science; QA1-939; 0202 electrical engineering, electronic engineering, information engineering; Meteorological Data; 0210 nano-technology; Mathematics

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.