基于IPSO-DE-MH算法的突发水污染事件预测模型参数识别

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。

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

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.