基于IPSO-DE-MH算法的突发水污染事件预测模型参数识别

李锦锦; 杨海东

doi:10.13205/j.hjgc.202206009

基于IPSO-DE-MH算法的突发水污染事件预测模型参数识别

doi: 10.13205/j.hjgc.202206009

李锦锦,
杨海东^,

福州大学经济与管理学院, 福州 350116

基金项目:

中国博士后特别资助项目(2018T110640,2016M602053)

福建省自然科学基金面上项目(2020J01460)

详细信息

作者简介:
李锦锦(1997-),女,研究生,主要研究方向为突发事件应急管理。yangcatifix@126.com

通讯作者:
杨海东(1981-),男,副教授,主要研究方向为复杂系统理论与方法、应急管理与应急物流。Jasperseu@fzu.edu.cn

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- 文章访问数: 117
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-22
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

SOURCE TRACKING OF WASTEWATER DISCHARGE INTO RIVERS USING HYDRODYNAMIC DIFFUSION WAVE MODEL AND GENETIC ALGORITHM

LI Jinjin,
YANG Haidong^,

School of Economics and Management, Fuzhou University, Fuzhou 350116, China

摘要

摘要: 预测模型是有效应对突发水污染事件的前提与基础。为了提高预测模型的准确性,提出了一种新的参数识别方法。首先从反问题与贝叶斯估计的视角构建突发水污染事件预测模型;然后在Metropolis-Hastings抽样方法的基础上,引入混沌理论、粒子群算法、微分进化算法等的思想,设计了一种新的参数识别方法,即IPSO-DE-MH算法;最后通过数值分析验证所设计方法的有效性和准确性。结果表明:新方法能较好地识别模型参数,为突发事件应急预测模型的快速构建提供了新思路。
- 参数识别 /
- 粒子群算法 /
- 微分进化算法 /
- Metropolis-Hastings抽样方法 /
- 混沌理论
Abstract: The prediction model is the premise and foundation of effectively dealing with sudden water pollution accidents.To improve the accuracy of the prediction model,a new parameters identification method was proposed in this paper.This paper first built a prediction model from the perspective of the inverse problem and Bayesian,and then designed a new identification method based on the chaos theory,particle swarm optimization,differential evolution and Metropolis-Hastings sampling method,i.e.IPSO-DE-MH.Finally,the effectiveness and accuracy of the designed method were verified by numerical analysis.The results showed that the new method could better identify the model parameters,and provide a new idea for the construction of an emergency prediction model.
- parameter identification /
- particle swarm optimization /
- differential evolution algorithm /
- Metropolis-Hastings sampling method /
- chaos theory

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参考文献(21)

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