Short-term forecasting of water demand is a crucial process for managing efficiently water supply systems. This paper proposes to develop a novel graph convolutional recurrent neural network (GCRNN) to predict time series of water demand related to some water supply systems or district metering areas that belong to the same geographical area. The aim is to build a graph-based model able to capture the dependence among the different water demand time series both in spatial and in temporal terms. This model is built on a set of different graphs, and its performance is compared to two methods, including a state-of-the-art deep long short-term memory (LSTM) neural network and a traditional seasonal autoregressive moving average model. Additionally, the forecasting model is tested in a condition when a sensor has a malfunction. The results show the ability of the GCRNN to produce accurate and reliable forecasting, especially when based on graph built while accounting for both time-series correlation and spatial criteria. The GCRNN consistently outperforms the LSTM during the fault test, showing its ability to generate a robust prediction for days after a sensor malfunction, given the GCRNN's ability to benefit from the other time series of the graph.

中文翻译：

---

用于水需求预测的图卷积递归神经网络

对水需求的短期预测是有效管理供水系统的关键过程。本文提出开发一种新颖的图卷积递归神经网络（GCRNN）来预测与属于同一地理区域的某些供水系统或区域计量区域相关的用水需求时间序列。目的是建立一个基于图形的模型，能够在空间和时间方面捕捉不同需水时间序列之间的依赖性。该模型建立在一组不同的图上，并将其性能与两种方法进行比较，包括最先进的深度长短期记忆 (LSTM) 神经网络和传统的季节性自回归移动平均模型。此外，预测模型在传感器发生故障的情况下进行测试。结果表明 GCRNN 能够产生准确和可靠的预测，尤其是在基于同时考虑时间序列相关性和空间标准的图形构建时。在故障测试期间，GCRNN 始终优于 LSTM，这表明 GCRNN 能够在传感器故障后的几天内生成稳健的预测，因为 GCRNN 能够从图中的其他时间序列中受益。