Population growth and urbanization worldwide entail the need for continuous renewal plans for urban water distribution networks. Hence, understanding the long-term performance and predicting the service life of water pipelines are essential for facilitating early replacement, avoiding economic losses, and ensuring safe transportation of drinking water from treatment plants to consumers. However, developing a suitable model that can be used for cases where data are insufficient or incomplete remains challenging. Herein, a new advanced meta-learning paradigm based on deep neural networks is introduced. The developed model is used to predict the risk index of pipe failure. The effects of different factors that are considered essential for the deterioration modeling of water pipelines are first examined. The factors include seasonal climatic variation, chlorine content, traffic conditions, pipe material, and the spatial characteristics of water pipes. The results suggest that these factors contribute to estimating the likelihood of failure in water distribution pipelines. The presence of chlorine residual and the number of traffic lanes are the most critical factors, followed by road type, spatial characteristics, month index, traffic type, precipitation, temperature, number of breaks, and pipe depth. The proposed approach can accommodate limited, high-dimensional, and partially observed data and can be applied to any water distribution system.

世界范围内的人口增长和城市化需要对城市供水网络进行持续更新计划。因此，了解供水管道的长期性能并预测其使用寿命对于促进早期更换、避免经济损失以及确保饮用水从处理厂安全输送到消费者至关重要。但是，开发可用于数据不足或不完整情况的合适模型仍然具有挑战性。在此，介绍了一种基于深度神经网络的新的高级元学习范式。开发的模型用于预测管道故障的风险指数。首先检查被认为对输水管道恶化建模必不可少的不同因素的影响。这些因素包括季节性气候变化，氯含量、交通状况、管材和水管的空间特征。结果表明，这些因素有助于估计配水管道发生故障的可能性。余氯的存在和车道数是最关键的因素，其次是道路类型、空间特征、月份指数、交通类型、降水、温度、中断次数和管道深度。所提出的方法可以容纳有限的、高维的和部分观测的数据，并且可以应用于任何配水系统。余氯的存在和车道数是最关键的因素，其次是道路类型、空间特征、月份指数、交通类型、降水、温度、中断次数和管道深度。所提出的方法可以容纳有限的、高维的和部分观测的数据，并且可以应用于任何配水系统。余氯的存在和车道数是最关键的因素，其次是道路类型、空间特征、月份指数、交通类型、降水、温度、中断次数和管道深度。所提出的方法可以容纳有限的、高维的和部分观测的数据，并且可以应用于任何配水系统。