A novel approach for identifying the source of contamination in a water supply network based on the random forest classifying algorithm is presented in this paper. The proposed method is tested on two different water distribution benchmark networks with different sensor placements. For each considered network, a considerable amount of contamination scenarios with randomly selected contamination parameters were simulated and water quality time series of network sensors were obtained. Pollution scenarios were defined by randomly generated pollution source location, pollution starting time, duration of injection and the chemical intensity of the pollutant. Sensor layout's influence, demand uncertainty and imperfect sensor measurements were also investigated to verify the robustness of the method. The proposed approach shows high accuracy in localizing the potential sources of pollution, thus greatly reducing the complexity of the water supply network contamination detection problem.

提出了一种基于随机森林分类算法的供水管网污染源识别新方法。所提出的方法在具有不同传感器位置的两个不同的水分配基准网络上进行了测试。对于每个考虑的网络，模拟了随机选择污染参数的大量污染场景，并获得了网络传感器的水质时间序列。污染情景由随机产生的污染源位置，污染开始时间，注入持续时间和污染物的化学强度定义。还研究了传感器布局的影响，需求不确定性和不完善的传感器测量，以验证该方法的鲁棒性。