Original scientific paper

https://doi.org/10.17559/TV-20230620000748

Traceability of River Water Pollution Based on MFO and M-H Algorithms

Dongyan Jia ; School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China; Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China *
Jinling Song ; School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China; Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Lisha Dong ; School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Yan Kang ; School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China; Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
Xiaoning Zeng ; Institution School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China

* Corresponding author.

Abstract

The work proposed a novel model to accurately trace the pollution sources of water pollution incidents based on moth-flame optimization and Metropolis-Hastings sampling algorithms. The model first utilized moth-flame optimization to estimate the parameters of the pollutant migration-diffusion model by minimizing the error between monitored and predicted concentration. It then traced the optimal pollution source location, discharge volume, and time using the M-H sampling algorithm. Simulation experiments demonstrated the model achieved significantly lower errors in tracing pollution source information compared to a previous method, with relative errors within 1.33%. The new model provides an accurate and efficient approach to tracing water pollution incidents and overcomes the limitations of previous methods. It exhibits substantial potential in identifying pollution sources within real-world aquatic environments as well as facilitating prompt responses to mitigate environmental and health impacts.

Keywords

M-H sampling; migration-diffusion model; moth-flame optimization algorithm; parameter calibration; traceability; water pollution

Hrčak ID:

316352

URI

https://hrcak.srce.hr/316352

Publication date:

23.4.2024.

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