| 突发性水污染事件溯源方法 |
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| 引用本文： | 杨海东,肖 宜,王卓民,邵东国,刘碧玉.突发性水污染事件溯源方法[J].水科学进展,2014,25(1):122-129. |
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| 作者姓名： | 杨海东 肖 宜 王卓民 邵东国 刘碧玉 |
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| 作者单位： | 1.武汉大学水资源与水电工程科学国家重点实验室, 湖北武汉 430072; |
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| 基金项目： | 国家水体污染控制与治理科技重大专项（2012ZX07205005）；水利部公益性行业科研专项经费资助项目（201101063） |
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| 摘 要： | 为快速准确地求解突发性水污染溯源问题，在微分进化与蒙特卡罗基础上提出了一种新的溯源方法。该方法将溯源问题视为贝叶斯估计问题，推导出污染源强度、位置和排放时刻等未知参数的后验概率密度函数；结合微分进化和蒙特卡罗模拟方法对后验概率分布进行采样，进而估计出这些未知参数，确定污染源项。通过算例与贝叶斯-蒙特卡罗方法进行对比，结果表明:该方法可使迭代次数有效缩减3/4，污染源强度、位置和排放时刻的平均相对误差分别减少1.23%、2.23%和4.15%，均值误差分别降低0.39%、0.83%和1.49%，其稳定性和可靠性明显高于贝叶斯-蒙特卡罗方法，能较好地识别突发性水污染源，为解决突发水污染事件中的追踪溯源难点问题提供了新的思路和方法。
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| 关 键 词： | 突发性水污染 贝叶斯推理 蒙特卡罗 微分进化 溯源 |
| 收稿时间： | 2013-03-05 |
On source identification method for sudden water pollution accidents |
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| YANG Haidong,XIAO Yi,WANG Zhuomin,SHAO Dongguo,LIU Biyu.On source identification method for sudden water pollution accidents[J].Advances in Water Science,2014,25(1):122-129. |
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| Authors: | YANG Haidong XIAO Yi WANG Zhuomin SHAO Dongguo LIU Biyu |
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| Affiliation: | 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;2.Lule? University of Technology, Lule? SE 97754, Sweden |
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| Abstract: | In order to solve the source identification problem of sudden water pollution accident accurately and quickly, a method based on the Differential Evolution and Markov Chain Monte Carlo (MCMC) is presented. First, the problem is considered as a Bayesian estimation problem, and the posterior probability distribution of the unknown parameters that include source’s position, intensity and events’ initial time are deduced with Bayesian inference. Second, these unknown parameters are estimated by sampling the posterior probability distribution using the Differential Evolution algorithm and Markov Chain Monte Carlo simulation, and the sources are further identified. To test the effectiveness and accuracy of the proposed method, numerical experiments are carried out, and the model result is compared to that of the Bayesian-MCMC method. The conclusions are as following: three fourth of the iterations can be reduced, the average relative error of the source’s position, intensity and events initial time are reduced 1.23%, 2.23% and 4.15%, their mean errors are decreased 0.39%,0.83% and 1.49% by using the proposed method. The latter is thus more stable and robust than the Bayesian-MCMC method, and is able to identify the sudden water pollution accidents’ source effectively. Therefore, this study provides a new approach and method to solve the difficult traceability problem of sudden water pollution accidents. |
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| Keywords: | sudden water pollution Bayesian inference Monte Carlo differential evolution source identification |
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