Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms
Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms
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基于 KELM 替代模型和四种启发式优化算法识别地下水污染物源
DOI：
10.1016/j.advwatres.2020.103540
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发表时间：
2020-04
影响因子：
4.7
通讯作者：
Lu Wenxi
中科院分区：
文献类型：
--
作者：
Zhao Ying;Qu Ruizhuo;Xing Zhenxiang;Lu Wenxi
识别地下水污染源需要通过监测几口观测井中的污染物浓度来反向确定污染源的特征。然而，由于识别的病态性和高耗时，一个有效的识别过程和准确的估计结果显得尤为重要。为了提高识别地下水污染源的效率，采用基于核的极限学习机代替耗时的模拟模型。采用四种启发式搜索算法提高识别结果的准确性。所提出的方法在假设和实际案例中都得到了检验。结论是：1。通过代理模型的正反向计算，可以显著降低识别地下水源的时间成本；2. 将传统的遗传算法和粒子群优化算法与量子计算相结合，提高了计算效率和精度；和3。通过使用多种搜索算法对实际案例中的未知污染源进行识别，可以得到每个污染源的释放历史范围，降低了单一算法识别结果的病态性，提高了识别结果的可靠性。
Identifying groundwater contaminant sources involves a reverse determination of the source characteristics by monitoring contaminant concentrations in a few observation wells. However, due to the ill-posed nature and high time consumption of identification, an efficient identification process with accurate estimated results is particularly important. To improve the efficiency of identifying groundwater contaminant sources, a kernel-based extreme learning machine was used as a surrogate for the time-consuming simulation model. Four heuristic search algorithms were used to improve the accuracy of the identification results. The proposed approach was tested in both hypothetical and actual cases. The conclusions are: 1. By forward and backward calculation of the surrogate model, the time cost of identifying groundwater sources can be reduced significantly; 2. When a traditional genetic algorithm and a particle swarm optimization algorithm are combined with quantum computing, computational efficiency and accuracy are both improved; and 3. By using various search algorithms to identify unknown contaminant sources in the actual case, the range of release histories of each contaminant source can be obtained, decreasing the ill-posed nature of the identification result obtained by a single algorithm and improving the reliability of the identification results.
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基于集合替代模型识别地下水污染物源的释放历史
影响因子：
6.4
作者：
Xing Zhenxiang;Qu Ruizhuo;Zhao Ying;Fu Qiang;Ji Yi;Lu Wenxi
通讯作者：
Lu Wenxi
DOI：
10.1016/j.compchemeng.2005.02.018
发表时间：
2005-05
期刊：
Comput. Chem. Eng.
影响因子：
--
作者：
Xiaotao Wan;J. Pekny;G. Reklaitis
通讯作者：
Xiaotao Wan;J. Pekny;G. Reklaitis
DOI：
10.4028/www.scientific.net/amr.875-877.2020
发表时间：
2014-02
期刊：
Advanced Materials Research
影响因子：
--
作者：
Yan Shi;L. J. Zhao;Jian Tang
通讯作者：
Yan Shi;L. J. Zhao;Jian Tang
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
影响因子：
3.6
作者：
Ayvaz, M. Tamer
通讯作者：
Ayvaz, M. Tamer