Recent advances in data-generating techniques led to an explosive growth of geo-spatiotemporal data. In domains such as hydrology, ecology, and transportation, interpreting the complex underlying patterns of spatiotemporal interactions with the help of deep learning techniques hence becomes the need of the hour. However, applying deep learning techniques without domain-specific knowledge tends to provide sub-optimal prediction performance. Secondly, training such models on large-scale data requires extensive computational resources. To eliminate these challenges, we present a novel distributed domain-aware spatiotemporal network that utilizes domain-specific knowledge with improved model performance. Our network consists of a pixel-contribution block, a distributed multiheaded multichannel convolutional (CNN) spatial block, and a recurrent temporal block. We choose flood prediction in hydrology as a use case to test our proposed method. From our analysis, the network effectively predicts high peaks in discharge measurements at watershed outlets with up to 4.1x speedup and increased prediction performance of up to 93\%. Our approach achieved a 12.6x overall speedup and increased the mean prediction performance by 16\%. We perform extensive experiments on a dataset of 23 watersheds in a northern state of the U.S. and present our findings.

翻译：近年来，数据生成技术的进步导致地理时空数据呈爆炸式增长。在水文、生态和交通等领域，借助深度学习技术解读时空交互中复杂的潜在模式因此成为当务之急。然而，直接应用深度学习技术而缺乏领域特定知识往往会导致预测性能次优。其次，在大规模数据上训练此类模型需要大量计算资源。为解决这些挑战，我们提出了一种新颖的分布式领域感知时空网络，该网络利用领域特定知识提升了模型性能。我们的网络包含一个像素贡献模块、一个分布式多头多通道卷积（CNN）空间模块和一个循环时间模块。我们选择水文中的洪水预测作为用例来测试所提出的方法。根据分析，该网络能有效预测流域出口流量测量中的高峰值，实现高达4.1倍的加速比，预测性能提升最高达93%。我们的方法实现了12.6倍的总体加速比，平均预测性能提升了16%。我们在美国北部一个州的23个流域数据集上进行了大量实验，并展示了研究结果。