Department of Fluid Mechanics and Computational Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia.
Center for Advanced Computing and Modelling, University of Rijeka, 51000 Rijeka, Croatia.
Sensors (Basel). 2021 Feb 6;21(4):1157. doi: 10.3390/s21041157.
This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node's start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.
本文提出并探讨了一种解决供水管网污染事件问题的新方法，该方法包括确定污染的确切来源、污染的开始和结束时间以及注入的污染物浓度。该方法基于将用于预测供水管网中最可能的污染源的机器学习算法与用于分别确定每个预测节点的污染开始时间、结束时间和注入污染物浓度的优化算法相结合。构建了两个略有不同的算法框架，它们都是基于所提到的方法。这两个算法框架都使用随机森林算法进行顶级源污染节点候选分类，其中一个框架直接使用随机烟花优化算法来分别确定每个预测节点的污染开始时间、结束时间和注入污染物浓度。第二个框架使用随机森林算法对每个顶级节点的开始时间、结束时间和污染物浓度进行额外的回归预测，然后与确定性全局搜索优化算法 MADS 耦合。使用一个具有完美传感器测量的小型（92 个潜在源）网络和一个具有模糊传感器测量的中型（865 个潜在源）基准网络来探索所提出的框架。这两个算法框架在确定真实源节点、开始和结束时间以及污染物浓度方面表现良好且稳健，第二个框架在模糊传感器测量基准网络上具有极高的效率。
