基于 MFO 和 M-H 算法的河水污染溯源能力

Dongyan Jia, Jinling Song, Lisha Dong, Yan Kang, Xiaoning ZENG
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引用次数: 0

摘要

:该作品提出了一种基于蛾焰优化和 Metropolis-Hastings 采样算法的新型模型,用于准确追踪水污染事件的污染源。该模型首先利用蛾焰优化法估计污染物迁移扩散模型的参数,使监测浓度与预测浓度之间的误差最小。然后,利用 M-H 采样算法追踪最佳污染源位置、排放量和时间。模拟实验表明,与之前的方法相比,该模型在追踪污染源信息方面的误差明显降低,相对误差在 1.33% 以内。新模型为追踪水污染事件提供了一种准确高效的方法,克服了以往方法的局限性。它在识别现实世界水生环境中的污染源以及促进及时响应以减轻环境和健康影响方面展现出巨大的潜力。
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Traceability of River Water Pollution Based on MFO and M-H Algorithms
: 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.
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