论文标题
水分配系统中的流量和压力预测的混合注意网络
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems
论文作者
论文摘要
由于复杂的空间和时间相关性，多元地理 - 感官时间序列序列预测具有挑战性。在城市水分配系统（WDS）中，已经部署了许多与空间相关的传感器来连续收集液压数据。监视流量和压力时间序列的预测对于操作决策，警报和异常检测至关重要。为了解决这个问题，我们提出了一个混合双阶段时空的基于注意的复发性神经网络（HDS-RNN）。我们的模型由两个阶段组成：一个基于空间注意的编码器和基于时间注意的解码器。具体而言，提出了沿时间和空间轴采用输入的混合空间注意机制。进行了实际数据集上的实验，并证明我们的模型在WDS中的流量和压力系列预测中的表现优于9个基线模型。
Multivariate geo-sensory time series prediction is challenging because of the complex spatial and temporal correlation. In urban water distribution systems (WDS), numerous spatial-correlated sensors have been deployed to continuously collect hydraulic data. Forecasts of monitored flow and pressure time series are of vital importance for operational decision making, alerts and anomaly detection. To address this issue, we proposed a hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN). Our model consists of two stages: a spatial attention-based encoder and a temporal attention-based decoder. Specifically, a hybrid spatial attention mechanism that employs inputs along temporal and spatial axes is proposed. Experiments on a real-world dataset are conducted and demonstrate that our model outperformed 9 baseline models in flow and pressure series prediction in WDS.
