Advances in Weather Forecasting Using Machine Learning Techniques

Abstract

Weather forecasting plays a crucial role in numerous sectors, ranging from agriculture and transportation to disaster management. Traditional meteorological models, while valuable, often face challenges in accurately predicting complex and dynamic weather patterns. This research paper explores the integration of machine learning (ML) techniques into weather forecasting to enhance predictive accuracy and reliability. The study begins by providing an overview of the limitations of conventional numerical weather prediction models and emphasizes the need for innovative approaches. It introduces a weather forecasting system based on machine learning, utilizing Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, Logistic Regression, and Naïve Bayes algorithms. The paper discusses the development and training of ML models using large datasets to capture intricate relationships among atmospheric variables. Among the evaluated models, Gradient Boosting achieves the highest predictive accuracy by effectively capturing nonlinear relationships and minimizing prediction errors. Performance evaluation demonstrates that integrating multiple machine learning techniques provides a stable, reliable, and scalable solution for short- to medium-term weather forecasting.