Real-time location algorithms of drinking water pollution sources based on domain knowledge

• Published in: Environmental science and pollution research international Vol. 28; no. 34; pp. 46266 - 46280
• Main Authors: Yan, Xuesong, Zhou, Zhengchen, Hu, Chengyu, Gong, Wenyin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Clustering; Domains; Drinking water; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Concerns and Pollution control in the Context of Developing Countries; Environmental Health; Environmental science; Optimization; Pollution detection; Pollution sources; Real time; Site selection; Waste Water Technology; Water Management; Water pollution; Water Pollution Control; Water quality; Water supply; Water supply systems; Real-time location algorithms; Pollution sources; Domain knowledge; Dynamic multi-mode optimization; Multi-strategy; Drinking water quality
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-13352-4

The real-time location of pollution sources is the process of inverting pollution sources based on the dynamic optimization model constructed by the time-varying pollution concentration detected by the water quality sensor. Due to the vast quantities of the water supply networks, the water quality sensors will only be placed on critical nodes, resulting in multiple solutions. However, the increased monitoring data enhances the uniqueness of the solution. Combined with the real-time location of pollution sources, this work proposed a multi-strategy dynamic multi-mode optimization algorithm based on domain knowledge, which could guide the population search and avoid trapped into local optimal. The merging mechanism was used to keep the diversity of the population and prevent sub-population clustering on the same optimal solution. The simulation results showed that the algorithm could effectively solve the real-time location problem of pollution sources in different pipe networks and pollution scenarios.