Temperature Prediction
Accurate temperature prediction is crucial for various sectors, from mitigating climate change impacts to optimizing energy consumption in buildings and manufacturing processes. Current research heavily utilizes machine learning, employing diverse architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers, often in hybrid combinations, to improve forecasting accuracy across various spatial and temporal scales. These advancements leverage diverse data sources, including meteorological data, remote sensing, and even simulations, to enhance predictive capabilities and address challenges like model uncertainty and interpretability, ultimately informing better decision-making in numerous applications.