Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis-Hastings sampling

Abstract

A quick and accurate identification of source information on sudden hazardous chemical leakage accident is crucial for early accident warning and emergency response. This study firstly regards source identification problem of sudden hazardous chemical leakage accidents as an inverse problem and presents a source identification model based on the Bayesian framework. Secondly, a new identification method is designed on the basis of particle swarm optimisation (PSO), differential evolution (DE) and the Metropolis-Hastings (M-H) sampling method. Lastly, the designed method, i.e. PSO-DE-MH, is verified by an outdoor experiment analyses in a section of the South-North Water Transfer Project. Results show that the number of iterations, the average absolute error, the average relative error and the average standard deviations of the identification results obtained by PSO-DE-MH are less than those of PSO-DE and DE-MH. Moreover, the relative error and the sampling relative error of the identification results under five different measurement errors (MEs) (σ = 0.01, 0.05, 0.1, 0.15, 0.2) are less than 9.5% and 0.2%, respectively. The designed method is effective even when the standard deviation of the ME increases to 0.2. Therefore, the designed method can effectively and accurately obtain the source information of sudden hazardous chemical leakage accidents. This study provides a new idea and method to solve the difficult problems of emergency management.

Keywords: Bayesian inference; Differential evolutionary; Emergency identification; Particle swarm optimisation; Sudden hazardous chemical leakage accidents.