Identification of pollution sources in river based on particle swarm optimization

The pollution sources identification model is presented by the coupling of the river water quality model and particle swarm optimization (PSO) algorithm to estimate pollution sources from the measured/simulated contaminant concentration in the river. The "twin experiment" is adopted to verify the feasibility of the identification model, and three cases are constructed to explore the results of the identification model in different situations. The experiment test demonstrated that the identification model is effective and efficient, while the model can accurately figure out the quantities of the pollutants and position of a single pollution source or multiple sources, with the relative error of the mean is less than 3%. Many factors are explored, including the level of random disturbance and the impact of particle population size. The outcome showed that the disturbance level is less than 5%, thus the precision is preferable, and when the number of particles is three, the identification is the best. When performing multiple sources, identification of multiple sets of monitoring sections respectively can obtain more accurate results with less error. In this paper, the optimization method of the inverse problem is applied to the identification of river pollution sources, which can help us to identify pollution sources and provide us a scientific basis for subsequent water pollution control and prevention.