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首页> 外文期刊>Journal of Hydroinformatics >Logistic regression analysis to estimate contaminant sources in water distribution systems
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Logistic regression analysis to estimate contaminant sources in water distribution systems

机译：逻辑回归分析以估计供水系统中的污染源

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摘要

Accidental or intentional contamination in a water distribution system (WDS) has recentlynattracted attention due to the potential hazard to public health and the complexity of thencontaminant characteristics. The accurate and rapid characterization of contaminant sources isnnecessary to successfully mitigate the threat in the event of contamination. The uncertaintynsurrounding the contaminants, sensor measurements and water consumption underscores thenimportance of a probabilistic description of possible contaminant sources. This paper proposes anrapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihoodnof any given node as a potential source of contamination. Not only does this algorithm yieldnlocation-specific probability information, but it can also serve as a prescreening step fornsimulation–optimization methods by reducing the decision space and thus alleviating thencomputational burden. The applications of this approach to two example water networks shownthat it can efficiently rule out numerous nodes that do not yield contaminant concentrations tonmatch the observations. This elimination process narrows down the search space of the potentialncontamination locations. The results also indicate that the proposed method efficiently yields angood estimation even when some noise is incorporated into the measurements and demandnvalues at the consumption nodes.
机译：由于对公共卫生的潜在危害以及污染物特性的复杂性，配水系统（WDS）中的偶然或故意污染引起了人们的关注。准确，快速地表征污染物源是成功减轻污染事件威胁的必要条件。围绕污染物的不确定性，传感器的测量值和水的消耗强调了可能的污染物来源的概率描述的重要性。本文提出了一种基于逻辑回归（LR）分析的快速估计方法，以估计任何给定节点作为潜在污染源的可能性。该算法不仅产生特定位置的概率信息，而且还可以通过减少决策空间并减轻计算负担而用作模拟优化方法的预筛选步骤。这种方法在两个示例性供水网络上的应用表明，它可以有效地排除许多节点，这些节点不会产生与观测值一致的污染物浓度。该消除过程缩小了潜在污染位置的搜索空间。结果还表明，即使在消耗节点的测量值和需求值中加入了一些噪声，该方法也能有效地产生良好的估计。

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