Application of stochastic programming in groundwater pollution source identification
Application of stochastic programming in groundwater pollution source identification
复制标题
随机规划在地下水污染源识别中的应用
DOI：
10.1080/15275922.2021.1892879
复制
发表时间：
2021-03
影响因子：
1.8
通讯作者：
Lu Wenxi
中科院分区：
文献类型：
--
作者：
Shi Chenxuan;Zhao Ying;Lu Wenxi
地下水污染是对生态环境和人类生活的严重威胁。在污染发生后，需要准确、高效地确定污染源的特征。确定污染源特征的主要依据是各观测井的污染物浓度数据。但是，由于监测井的布置、观测井点的噪声强度以及含水层参数的未知，地下水污染源的反演结果会受到很大影响。因此，如何减小其对反演结果的影响是本文研究的重点。本文介绍了一种随机规划优化模型，以探讨其在复杂情况下的误差控制能力。结果表明：在均质含水层中，模拟优化方法产生的归一化误差(NE%)稳定在2%左右，随机规划方法在60%∼95%的置信度区间内产生的归一化误差在0.20%~5.42%之间。此外，随机规划模型能够有效地控制噪声的影响。在模拟优化模型中，当噪声强度为0.1 ∼ 0.5时，NE值为2.01%∼12.68%，随机规划模型对应的NE值为0.12%∼11.87%。最后，本文考虑了含水层参数未知的情况(含水层参数和地下水污染源的同时识别)。结果表明，随着未知含水层参数个数的增加，模拟优化模型的NE从1.16%逐渐增加到8.75%。在置信度为80%时，随机规划模型的NE值比模拟优化模型降低了30%。
Abstract Groundwater pollution is a serious threat to the ecological environment and human life. It is necessary to determine the characteristics of pollution sources accurately and efficiently after the occurrence of pollution. The main basis for determining the characteristics of pollution sources is the pollutant concentration data of each observation well. However, due to the layout of the monitoring wells, the noise intensity of observation well concentration and unknown aquifer parameters, inversion results of groundwater pollution sources will be influenced significantly. Therefore, this article focuses on how to reduce its influence on the inversion results. In this article, a stochastic programming optimization model is introduced to explore its ability to control errors in complex situations. Results show that: in homogeneous aquifer, the normalized error (NE%) produced by simulation-optimization method is stable at about 2%, and the NE generated by stochastic programming method in confidence interval of 60%∼95% is between 0.20% and 5.42%. Moreover, stochastic programming model can effectively control the influence of noise. In the simulation-optimization model, when the noise intensity is 0.1 ∼ 0.5, the NE value is 2.01%∼12.68%, and the corresponding NE of stochastic programming model is 0.12%∼11.87%. Finally, this article considers the case that aquifer parameters are unknown (simultaneous identification of aquifer parameters and groundwater pollution sources). The results show that with the increase of the number of unknown aquifer parameters, the NE of the simulation-optimization model gradually increases from 1.16% to 8.75%. The NE value of the stochastic programming model decreases by 30% compared with the simulation-optimization model when the confidence level is 80%.
登录
查看更多内容
基于混合粒子群优化-极限学习机的地下水污染源识别
影响因子：
6.4
作者：
Li Jiuhui;Lu Wenxi;Wang Han;Fan Yue;Chang Zhenbo
通讯作者：
Chang Zhenbo
DOI：
10.7343/as-2019-427
发表时间：
2019-12
期刊：
Acque Sotterranee - Italian Journal of Groundwater
影响因子：
--
作者：
Dario Covucci
通讯作者：
Dario Covucci
DOI：
10.3389/fenvs.2020.00142
发表时间：
2020-09
期刊：
--
影响因子：
--
作者：
L. Colombo;L. Alberti;Pietro Mazzon;M. Antelmi
通讯作者：
L. Colombo;L. Alberti;Pietro Mazzon;M. Antelmi
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
Dw Wang;James Wang;Hf Wang;Ry Zhang;Z. Guo
通讯作者：
Dw Wang;James Wang;Hf Wang;Ry Zhang;Z. Guo
影响因子：
1.8
作者：
Atmadja, J;Bagtzoglou, AC
通讯作者：
Bagtzoglou, AC