Identification of source information for sudden water pollution incidents in rivers and lakes based on variable-fidelity surrogate-DREAM optimization

Abstract

For sudden water pollution incidents in rivers and lakes, the ability to quickly identify the pollution source is of great importance for providing early accident warning and implementing emergency control measures. Based on Bayesian reasoning, a variable-fidelity surrogate-differential evolution adaptive metropolis optimization(DREAM) optimization model for coupled inversion process is established in the posterior space of the pollution source.In order to verify the effectiveness of the algorithm, this paper takes lake A as the research area, and gives a hypothetical water pollution emergency, the pollution source location, release time and released mass of water pollutants suddenly released into water bodies were determined according to the method proposed in this paper. The results show that in the case of ensuring the accuracy of calculation, the algorithm can accelerate more than 200 times and effectively improves the computational efficiency of the traditional method for obtaining the source information of sudden water pollution events.