Comprehensive Analysis of Weather Forecasting Techniques

Weather forecasting plays a pivotal role in numerous sectors, from agriculture to disaster management, yet traditional methods often face limitations in accuracy and scalability. In response, machine learning (ML) techniques have emerged as potent tools for revolutionizing weather prediction. This review provides a comprehensive overview of ML applications in weather forecasting, delving into algorithms like regression, classification, and neural networks, which exploit vast datasets to capture intricate spatiotemporal patterns in weather phenomena. Ensemble learning strategies further enhance forecast accuracy by amalgamating multiple models. However, challenges persist, including data quality issues and computational demands. Overcoming these obstacles requires innovative approaches, such as integrating ML with physical models and developing explainable AI techniques. Future research directions also include exploring new data sources like remote sensing and social media data. By harnessing the synergy between ML and weather forecasting, we can advance predictive capabilities, aiding decision-making and bolstering resilience against extreme weather events.