Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

深度学习技术和算法正在成为一种颠覆性技术，具有改变全球经济、环境和社会的潜力。它们通常被应用于城市水系统的规划和管理问题，但是，缺乏对深度学习应用现状的系统评价，以及对深度学习有助于解决城市水挑战的潜在方向的检查。在这里，我们提供了这样的评论，涵盖了需水量预测、泄漏和污染检测、下水道缺陷评估、废水系统状态预测、资产监测和城市洪水。我们发现深度学习技术的应用仍处于早期阶段，因为大多数研究使用基准网络、合成数据、实验室或试点系统来测试深度学习方法的性能，但没有报告实际采用。与审查的其他问题相比，泄漏检测可能处于城市供水系统日常运营和管理实际实施的最前沿。五个研究挑战，即数据隐私、算法开发、可解释性和可信赖性、多智能体系统和数字孪生，被确定为推进深度学习在城市水管理中的应用和实施的关键领域。深度学习系统的未来研究和应用有望推动城市水系统向高智能和自治方向发展。