Design and application of deep neural network prediction model for regional rainstorm in Guangxi
Following a typical application process of AI technology,and starting from determining the data representation of the research object,this article discusses the construction process of a deep learning prediction model for"regional rainstorm"from using testing and selecting algorithmic tools,to adjusting and optimizing the model hyperparameters,enhancing the generalization of the model's performance,and ultimately achieving its deployment online,thus successfully making sensible predictions of the"Dragon-boat Rain"event in Guangxi in 2023.The results show that the practical performance of the model can be significantly enhanced by employing AI algorithms that can simulate the weather analysis process and provide reasonable explanations for prediction mechanisms based on an in-depth understanding of the characteristics of the algorithm and model architecture in combination with the actual demand for forecasting services.The application of TimeDistributed layers to encapsulate the temporal dimension of samples,which first extracts the spatial and related features of meteorological elements before further learning about temporal changes,is a meteorological AI model construction method that aligns with weather analysis approaches and shows better predictive performance.For"low probability"meteorological events of interest,it is an effective technical means to improve the performance of weather AI models by appropriately lowering meteorological evaluation indicators when setting event labels,enhancing sample data based on similar weather situations,and guiding the model to carry out the positive enhancement training for the focus of attention,so as to achieve a basic classification model for qualitative prediction of"with/without regional rainstorm occurrence".
万方数据
维普
