Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modelling. A general Bayesian framework is proposed that c...

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Bibliographic Details
Main Authors: Wang, Wei, Ji, Chao, Li, Chuanqi, Wu, Wenxin, Anak Gisen, Jacqueline Isabella
Format: Article
Language:English
Published: Springer Heidelberg 2025
Subjects:
QA Mathematics
TA Engineering (General). Civil engineering (General)
TD Environmental technology. Sanitary engineering
Online Access:http://umpir.ump.edu.my/id/eprint/44815/
http://umpir.ump.edu.my/id/eprint/44815/1/Source%20identification%20in%20river%20pollution%20incidents%20using%20a%20cellular.pdf

Internet

http://umpir.ump.edu.my/id/eprint/44815/
http://umpir.ump.edu.my/id/eprint/44815/1/Source%20identification%20in%20river%20pollution%20incidents%20using%20a%20cellular.pdf

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