Groundwater flooding (or infiltration) in sewer systems leads to significant negative consequences such as discharge of untreated sewage, reduction of system capacity, structural deterioration, and dilution of the wastewater stream delivered to a treatment plant causing malfunction. Cities with aging networks along coastal areas, where aquifers are shallow, are particularly vulnerable. Rehabilitation is necessary to mitigate the negative impact of infiltration but costly. Therefore, a prioritization strategy of intervention is required. This paper presents a decision-support model to identify the probability of infiltration into aging sewer when observations of infiltration and sewer conditions are sparse and time-limited. The model is based on logistic regression, where the variables are: material, soil, water table, and pipe size and shape. As a proof-of-concept, the method was applied to the city of Hoboken, NJ. Machine learning was used to calibrate, validate, and test the model using infiltration measurements, provided by the water authority. Upon calibration, model predictions agree well with the measurements with an accuracy of 82%. Sensitivity analysis of the model was carried out and shows that the most important parameter is the water table of the shallow aquifer. Overall, the proposed approach can be a valuable tool for strategic intervention of sewer repair and flood mitigation in urban areas.

下水道系统中的地下水泛滥（或渗透）会导致严重的负面后果，例如排放未经处理的污水、系统容量降低、结构恶化以及输送到处理厂的废水流被稀释导致故障。沿海地区网络老化、含水层较浅的城市尤其容易受到攻击。恢复是必要的，以减轻渗透的负面影响，但代价高昂。因此，需要一个优先干预策略。本文提出了一种决策支持模型，用于在对渗透和下水道条件的观察稀疏且有时间限制的情况下识别老化下水道渗透的概率。该模型基于逻辑回归，其中变量为：材料、土壤、地下水位以及管道尺寸和形状。作为概念验证，该方法应用于新泽西州霍博肯市。机器学习用于使用由水务局提供的渗透测量来校准、验证和测试模型。校准后，模型预测与测量结果非常吻合，准确度为 82%。对该模型进行了敏感性分析，结果表明最重要的参数是浅层含水层的地下水位。总体而言，所提出的方法可以成为城市地区下水道修复和防洪战略干预的宝贵工具。模型预测与测量结果非常吻合，准确率为 82%。对该模型进行了敏感性分析，结果表明最重要的参数是浅层含水层的地下水位。总体而言，所提出的方法可以成为城市地区下水道修复和防洪战略干预的宝贵工具。模型预测与测量结果非常吻合，准确率为 82%。对该模型进行了敏感性分析，结果表明最重要的参数是浅层含水层的地下水位。总体而言，所提出的方法可以成为城市地区下水道修复和防洪战略干预的宝贵工具。