mikubill/gan-convlstm

GAN-ConvLSTM is a research project that tries to predict rainfall using artificial intelligence. In simple terms, it looks at past weather radar images and attempts to generate a forecast of what the rain will look like in the near future. The project is labeled as a "Trial Version," meaning it is an experimental approach rather than a finished, ready-to-use weather forecasting product. The project combines two different AI techniques to achieve this. The first part is a prediction model that learns patterns from sequences of past weather images to estimate future ones. The second part acts as a quality checker, determining whether the predicted weather images look realistic or fake. By pitting these two parts against each other, the system learns to produce more visually convincing rainfall predictions than it would with the prediction model alone. This project would mainly appeal to meteorology researchers, data scientists, or students studying AI applications in weather forecasting. For example, a researcher exploring new ways to predict sudden downpours could use this approach as a starting point. However, because it is an experimental trial, it is not meant for operational meteorologists who need reliable, real-world forecasting tools today. The documentation is extremely sparse. Beyond the high-level architecture diagrams, the README doesn't go into detail about how to set up the project, what data format it requires, or how well the predictions actually perform. Anyone looking to use it would need a solid understanding of Python and deep learning to fill in the gaps and make it run.