Contaminant intrusion in water distribution networks affects the safety of residential drinking water, and sensors are placed to detect a contaminant intrusion. As the number and location of sensors directly affect the detection efficiency, several studies focused on sensor placement optimization. Sensor placement optimization is an important mitigation measure, which is necessary to reduce the serious consequences of contaminant intrusion as different nodes impact differently on the water distribution networks after contaminant intrusion, that is, the risk levels of contaminant intrusion at nodes are different. Existing studies have considered changes in the probability of node contamination, but this probability is primarily based on the properties of the node itself. This study proposes a multi-objective sensor placement optimization method based on contamination risks, which involves the following steps. (1) Four different types of contamination probabilities are defined in terms of the impact of the contamination events. This is followed by solving a multi-objective optimization problem to obtain the Pareto fronts that are based on a variety of assumptions for the contamination probability distribution. (2) The Pareto fronts are ranked and clustered through a multi-criteria decision analysis, which leads to the optimal scheme in each cluster under different preferences. (3) The optimal schemes are compared in terms of the impact of the contaminant intrusion on the network. The proposed method is empirically evaluated by using the D-town network model. The results reveal that when node-to-node variation in the contamination probability is enabled, each sensor placement scheme exhibits a high true detection rate of contamination. The contamination probability based on the number of affected pipelines can yield optimal sensor placement for a fixed number of sensors to minimize the impact of contaminant intrusion on the pipeline network.

配水网络中的污染物侵入会影响住宅饮用水的安全，因此放置传感器以检测污染物侵入。由于传感器的数量和位置直接影响检测效率，一些研究集中在传感器放置优化上。传感器放置优化很重要由于不同节点在污染物侵入后对配水管网的影响不同，即节点处污染物侵入的风险等级不同，这对于减少污染物侵入的严重后果是必要的。现有的研究已经考虑了节点污染概率的变化，但这个概率主要是基于节点本身的属性。本研究提出了一种基于污染风险的多目标传感器放置优化方法，包括以下步骤。(1) 根据污染事件的影响，定义了四种不同类型的污染概率。然后求解一个多目标优化问题，得到基于污染概率分布的各种假设的帕累托前沿。(2)通过多准则决策分析对Pareto前沿进行排序和聚类，从而得出不同偏好下每个聚类中的最优方案。(3)从污染物入侵对网络的影响方面比较了最优方案。通过使用 D-town 网络模型对所提出的方法进行了经验评估。结果表明，当启用污染概率的节点间变化时，每个传感器放置方案都表现出较高的污染真实检测率。基于受影响管道数量的污染概率可以为固定数量的传感器产生最佳传感器放置，以最大限度地减少污染物侵入对管道网络的影响。