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

Abstract

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.摘要卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元, 通过将循环机制与卷积运算相结合, 常常用于时空序列的预测. 为了明确上述两种模型的收敛速度和分类能力, 需要使用相同的模型架构对相同的分类问题进行预测. 本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题, 使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估. 结果表明, ConvGRU的收敛速度比 ConvLSTM快约25%. ConvLSTM和ConvGRU的预警性能随地区, 时间, 降雨强度的变化趋势相似, 但大部分ConvLSTM的得分较高, 少数情况下ConvGRU的得分较高.