River and canal sudden water pollution tracing based on the Metropolis-Hastings algorithm

Junhu Jia; Youfu Jiang; Ming Yang; Kaihao Hu

doi:10.1117/12.3026787

27 March 2024 River and canal sudden water pollution tracing based on the Metropolis-Hastings algorithm

Junhu Jia, Youfu Jiang, Ming Yang, Kaihao Hu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310541 (2024) https://doi.org/10.1117/12.3026787
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In response to sudden water pollution incidents in rivers and canals, a pollution source tracing algorithm is proposed, employing the Markov Chain Monte Carlo (MCMC) method for rapid identification. This algorithm converts the traceability problem into a Bayesian estimation issue and utilizes the Metropolis-Hastings (M-H) sampling algorithm to sample the posterior probability density function. Consequently, it provides probability distributions for the location, time, and mass of pollutants in river canals.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhu Jia, Youfu Jiang, Ming Yang, and Kaihao Hu "River and canal sudden water pollution tracing based on the Metropolis-Hastings algorithm", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310541 (27 March 2024); https://doi.org/10.1117/12.3026787

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