Advancements in Weather Forecasting through Machine Learning Algorithms

Abstract

This research paper investigates the integration of Machine Learning (ML) algorithms in weather forecasting, exploring Decision Trees, Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting. It addresses challenges in traditional methods and how ML can learn complex patterns directly from data. It discusses feature selection, preprocessing, and model evaluation, showcasing case studies and real-world applications. Comparing ML with conventional techniques, it highlights accuracy, efficiency, and scalability, envisioning a future of more accurate and timely forecasts. This synthesis of current knowledge identifies gaps and proposes future directions for advancing the field, aiming to benefit society through improved preparedness and decision-making in response to weather events.