Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Center for Advanced Computing and Modelling, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia.
Sensors (Basel). 2021 Jan 1;21(1):245. doi: 10.3390/s21010245.
In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.
在供水管网污染事件中，已有多项研究考虑了不同的方法来确定污染情景信息。如果能确切知道污染物注入位置的数量，那将是非常有益的，因为有些方法只能应用于单个注入位置，而其他方法的效率更高。在这项工作中，使用神经网络和随机森林分类算法来预测污染物注入位置的数量。该预测模型是使用从随机注入起始时间、持续时间、浓度值和注入位置数量（从 1 到 4 不等）的模拟污染事件场景中获得的数据进行训练的。通过分类来确定是否发生了单个或多个注入位置，并预测确切的注入位置数量。为两个不同的基准网络，即中型网络 Net3 和大型里士满网络，获取了数据。此外，还对传感器布局、需求不确定性和模糊传感器对模型准确性的影响进行了研究。所提出的方法在预测供水管网中是否发生单个或多个污染物注入方面具有出色的准确性，并且在预测确切的注入位置数量方面也具有良好的准确性。
