Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

• Published in: Applied water science Vol. 7; no. 4; pp. 1955 - 1963
• Main Authors: Zhang, Shou-ping, Xin, Xiao-kang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2017; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Comparative Law; Dimensional analysis; Earth and Environmental Science; Earth Sciences; Emergencies; Emergency response; Errors; Genetic algorithms; Hydrogeology; Identification; Identification methods; Industrial and Production Engineering; International & Foreign Law; Mathematical analysis; Mathematical models; Nanotechnology; Objective function; Optimization; Original Article; Point source pollution; Pollutants; Pollution; Pollution sources; Population number; Private International Law; Rescue operations; Rivers; Searching; Sound; Stream pollution; Waste Water Technology; Water Industry/Water Technologies; Water Management; Water pollution; Water Pollution Control; Water quality; Pollutant amount; Pollution source position; Identification model; Water pollution incident; Basic genetic algorithm (BGA)
• ISSN: 2190-5487; 2190-5495
• DOI: 10.1007/s13201-015-0374-z

Identification of pollutant s ources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.