Accurate Precipitation Prediction Model Based on Deep Learning Algorithm

In the context of global climate change, extreme weather events occur frequently, causing serious impacts on society, economy, and people's lives. Therefore, improving the accuracy of precipitation forecasting is of great significance. This article intends to combine multi-source weather information with long short-term memory networks to propose a deep learning based precipitation accurate prediction model, which can accurately describe the temporal and spatial distribution of rainfall events. The error rate shows regional differences, for example, the error rate for predicting heatwaves in Europe is as low as 10%, while the error rate for predicting droughts in Africa is as high as 18%. The research results of this article will help improve the accuracy of rainfall forecasting and provide new ideas and methods for using deep learning for complex weather forecasting in disaster prevention and reduction, agricultural planning, water management, and other areas.