Multi-Class Weather Forecasting: A Review

Various deep learning structures have been created to handle the complexities found in time series datasets within the field of weather forecasting. This review explores the latest advancements in deep-learning-based weather forecasting, examining Neural Network architecture design, spatial and temporal scales, as well as datasets and benchmarks. The focus then shifts to the achieved outcomes, emphasizing reported accuracy and the prediction scale, assessing whether the model is suitable for local or regional areas and if it performs well in short-term or long-term predictions. With today's technology, we can use electronic systems to look at pictures of the weather and make guesses about what it will be like. Recently, people have been talking a lot about AI, ML, and DL in this area. These are ways to make machines smart and help them learn. To make weather predictions better, we can use deep learning, which is a type of machine learning. Once set up, deep learning doesn't need a lot of human help.