An intelligent traceability method of water pollution based on dynamic multi-mode optimization

Abstract

Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source’s characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution’s real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes’ distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions’ number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm’s historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm’s effectiveness in solving problems—accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.

River and canal sudden water pollution tracing based on the Metropolis-Hastings algorithm

“…Tracing sudden water pollution incidents involves identifying the pollution source through various methods after a river water pollution event. Researchers have developed different algorithms for tracing river water pollution accidents, broadly categorized into tracer [1], multivariate statistics [2], and search and location methods [3], based on their principles and implementations. Applying the method of inverse problem derivation for pollution sources tracing involves deducing key pollution parameters from information like pollutant concentrations obtained after the incident.…”

Special issue on 2021 international conference on machine learning and big data analytics for iot security and privacy

“…Wang et al [3] puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multimode optimization was designed and proposed in the work [4]. Key technologies for a digital twin-based shop floor management and control system are investigated, and the concept is designed and implemented by Peng and Zheng [5].…”