Identification of source information for sudden water pollution incidents in rivers and lakes based on variable-fidelity surrogate-DREAM optimization. (November 2020)

• Record Type: Journal Article
• Authors: Wu, Wei; Ren, Jucheng; Zhou, Xiaode; Wang, Jiawei; Guo, Mengjing
• Is Part Of: Environmental modelling & software. Volume 133(2020)
• Publication Date: 2020-11
• Subjects: Variable-fidelity surrogate -- Differential evolution adaptive metropolis optimization(DREAM) -- Sudden water pollution -- Identification of the pollution source; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104811
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: A variable-fidelity surrogate model is used to determine the source of sudden water pollution incidents in rivers and lakes. A new sample point is added during the update of the surrogate model. Using the surrogate model instead of the physical model reduces the computation time by a factor 200.