Institute of Telecommunications, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.
Blue Technologies sp. z o.o., ul. Puławska 266/221, 02-684 Warsaw, Poland.
Sensors (Basel). 2021 May 14;21(10):3426. doi: 10.3390/s21103426.
Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network-e.g., through leakages leading to groundwater reservoirs-and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.
下水道网络中非法排放的腐蚀性污染物可能会扩散到下水道网络之外，例如通过导致地下水储存库的泄漏，并可能破坏污水处理厂的正常运行。因此，这些污染物对水体、自然环境以及所有生命构成严重威胁。在本文中，我们专注于识别污水污染物并定位污水网络中其源点的问题，给定通过位于污水网络各处的传感器收集的污水时间序列测量值。我们通过解决两个关联的子问题来提供解决方案。第一个子问题涉及污水中流动污染物的检测和识别，即评估给定的时间序列是否对应于污染事件，并确定污染物是什么造成的。这个问题使用随机森林分类器来解决。第二个子问题涉及在考虑物质识别子问题的结果时，估计测量点与污染源之间的距离。XGBoost 算法用于预测从源头到传感器的距离。这两个模型都使用欧洲城市次流域污水的模拟电导率和 pH 测量值进行训练。我们的实验表明：(a) 识别子问题解决方案的精度和召回率都可以高达 96%，并且 (b) 获得的源位置估计子问题的误差中位数可以低至 6.30 米。
