Comparative study on the performance of ConvLSTM and ConvGRU in classification problems—taking early warning of short-duration heavy rainfall as an example

Convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) are two widely adopted deep learning models that combine recurrent mechanisms with convolutional operations for spatiotemporal sequences forecasting. To clarify the convergence speed and classification ability of the above two models, using the same model architecture to predict the same classification problem is necessary. This research treats the district-level warning of short-duration heavy rainfall in Beijing as a binary classification problem in deep learning, and composite radar reflectivity data of the Beijing–Tianjin–Hebei radar network and rainfall data from automatic weather stations in Beijing are used for training and performance evaluation. The results show that the convergence speed of ConvGRU is approximately 25% faster than that of ConvLSTM. The early-warning performances of ConvLSTM and ConvGRU have similar trends with region, time, and rain intensity, but most of the scores of ConvLSTM are higher, and in a few cases, ConvGRU has higher scores.