论文标题
一个多任务的两流时空时空卷积神经网络，用于对流风暴。
A Multi-task Two-stream Spatiotemporal Convolutional Neural Network for Convective Storm Nowcasting
论文作者
论文摘要
对流风暴现象的目的是对严重和迫在眉睫的对流风暴的当地预测。在这里，我们从机器学习的角度考虑了对流风暴现象的问题。首先，我们使用像素的采样方法来构建空间时空特征，并在训练集中灵活地调整培训中正和负样本的比例，以减轻类别不足的问题。其次，我们采用简洁的两流卷积神经网络来提取空间和时间提示以进行现状。这简化了网络结构，减少了训练时间的要求并提高了分类准确性。两流网络同时使用了雷达和卫星数据。在由此产生的两流融合卷积神经网络中，将某些参数输入到单流卷积神经网络中，但是它可以学习许多数据的功能。此外，考虑到分类和回归任务的相关性，我们制定了一种多任务学习策略，可以预测此类任务中使用的标签。我们将两流多任务学习集成到单个卷积神经网络中。鉴于紧凑的体系结构，该网络比现有的复发神经网络更有效，更容易优化。
The goal of convective storm nowcasting is local prediction of severe and imminent convective storms. Here, we consider the convective storm nowcasting problem from the perspective of machine learning. First, we use a pixel-wise sampling method to construct spatiotemporal features for nowcasting, and flexibly adjust the proportions of positive and negative samples in the training set to mitigate class-imbalance issues. Second, we employ a concise two-stream convolutional neural network to extract spatial and temporal cues for nowcasting. This simplifies the network structure, reduces the training time requirement, and improves classification accuracy. The two-stream network used both radar and satellite data. In the resulting two-stream, fused convolutional neural network, some of the parameters are entered into a single-stream convolutional neural network, but it can learn the features of many data. Further, considering the relevance of classification and regression tasks, we develop a multi-task learning strategy that predicts the labels used in such tasks. We integrate two-stream multi-task learning into a single convolutional neural network. Given the compact architecture, this network is more efficient and easier to optimize than existing recurrent neural networks.
