Abstract This paper is concerned with the hydraulic performance assessment of large scale water distribution networks in presence of uncertainty. In particular, the associate connectivity detection problem is examined in detail. For this purpose, a Bayesian system identification methodology is combined with an efficient hydraulic simulation model. A number of hydraulic model classes are defined as potential connectivity events. Based on information from flow rates in the pipes, the proposed updating technique provides estimates of the most probable connectivity scenarios. Such scenarios correspond to the model classes that maximize their evidences or posterior probabilities. The effectiveness of the proposed identification framework is illustrated by applying the connectivity detection approach to a real water distribution system.

中文翻译：

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复杂配水网络水力性能评估的随机框架：在连通性检测问题中的应用

摘要 本文涉及存在不确定性的大型配水网络的水力性能评估。特别是，详细检查了关联连接检测问题。为此，将贝叶斯系统识别方法与高效的水力模拟模型相结合。许多水力模型类被定义为潜在的连通性事件。根据来自管道中流速的信息，建议的更新技术提供了最可能的连通性场景的估计。此类场景对应于最大化其证据或后验概率的模型类。通过将连通性检测方法应用于真实的配水系统，说明了所提出的识别框架的有效性。