论文标题
深度多站天气预测：可解释的循环卷积神经网络
Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks
论文作者
论文摘要
由于数据驱动的模型取得的进步，应用于天气预报的深度学习已经开始越来越受欢迎。本文比较了两种不同的深度学习体系结构，以对从欧洲18个城市收集的每日数据进行天气预测，并在15年内跨越。我们提出了深度关注的Unistream Multistream（DAUM）网络，该网络研究了不同类型的输入表示（即，张力的Unistream vs. Multistream）以及注意机制的融合。特别是，我们表明，在模型中添加一个自我发挥作用会提高整体预测性能。此外，使用可视化技术，例如遮挡分析和得分最大化，以提供对预测目标城市特定目标特征的最重要特征和城市的额外见解。
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. We propose the Deep Attention Unistream Multistream (DAUM) networks that investigate different types of input representations (i.e. tensorial unistream vs. multistream ) as well as the incorporation of the attention mechanism. In particular, we show that adding a self-attention block within the models increases the overall forecasting performance. Furthermore, visualization techniques such as occlusion analysis and score maximization are used to give an additional insight on the most important features and cities for predicting a particular target feature of target cities.
