Overview of Weather Prediction with ML

Abstract

Weather forecasting is pivotal in critical sectors such as agriculture, disaster management, air traffic control, and marine navigation. Accuracy predictions ensure public safety, resource optimization, and sustainable development. Traditional forecasting methods, predominantly based on physical and statistical models, often encounter significant challenges in maintaining accuracy over extended periods due to the rapid and complex changes in climatic patterns. To address these limitations, this study explores the application of ensemble learning, a machine learning technique that combines multiple models, including Artificial Neural Networks (ANNs), Random Forests, and Support Vector Machines (SVMs). By integrating the strengths of individual models, ensemble learning offers a robust and adaptive framework for improving the accuracy and reliability of weather predictions. The research highlights the advantages of ensemble learning in mitigating common issues like overfitting and underfitting while reducing computational costs and improving resilience to data variability. Results demonstrate its potential to deliver more reliable and cost-effective forecasts, which are critical for disaster preparedness, energy management, and transportation logistics decision-making. This study underscores the transformative potential of ensemble learning in modern meteorology, offering a scalable and adaptive approach to address the growing challenges posed by rapidly changing climatic conditions.