Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine
Groundwater contamination source identification based on a hybrid particle swarm optimization-extreme learning machine
复制标题
基于混合粒子群优化-极限学习机的地下水污染源识别
DOI：
10.1016/j.jhydrol.2020.124657
复制
发表时间：
2020-05
影响因子：
6.4
通讯作者：
Chang Zhenbo
中科院分区：
文献类型：
--
作者：
Li Jiuhui;Lu Wenxi;Wang Han;Fan Yue;Chang Zhenbo
当模拟优化方法应用于地下水污染源识别（GCSI）时，通常将数值模拟模型作为约束条件嵌入到优化模型中。成百上千次的仿真模型调用在求解优化模型时往往会导致计算灾难。建立仿真模型的代理模型可以有效缓解这一弊端。使用极限学习机（ELM）为仿真模型建立代理模型时，使用随机给定的输入权重和隐藏层偏差来计算输出权重，这可能会导致代理模型的准确性不足，并且对训练集中未出现的样本的泛化能力不足。然而，通过将ELM与粒子群优化（PSO）相结合，PSO可以用于优化ELM的输入权值和隐藏层偏差的选择；因此，计算输出权重并建立基于PSO-ELM的代理模型，而不是嵌入优化模型的仿真模型来完成GCSI。当模拟优化方法应用于GCSI时，很少考虑参数的不确定性。因此，基于PSO-ELM的GCSI考虑了参数的不确定性。结果表明，与ELM相比，PSO-ELM能够建立精度更高的代理模型。基于PSO-ELM的代理模型可以嵌入到优化模型中，有效解决GCSI问题。基于PSO-ELM的预测模型可以预测不同随机参数对应的污染源的释放历史。
When the simulation-optimization method is applied to groundwater contamination source identification (GCSI), the numerical simulation model is usually embedded in the optimization model as a constraint condition. Hundreds and thousands of simulation model calls, while solving the optimization model, often lead to computational disaster. Establishing a surrogate model for the simulation model can effectively alleviate this drawback. When using extreme learning machine (ELM) to establish a surrogate model for a simulation model, the output weights are calculated using randomly given input weights and hidden-layer deviations, which may lead to inadequate accuracy of the surrogate model and insufficient generalization ability to samples not appearing in the training set. However, by combining the ELM with particle swarm optimization (PSO), the PSO can be used to optimize the selection of the input weights and hidden-layer deviations of the ELM; thus, calculating the output weights and establishing a surrogate model based on the PSO-ELM instead of the simulation model embedded in the optimization model to complete the GCSI. The uncertainty of the parameters is rarely considered when the simulation-optimization method is applied to GCSI. Thus, the uncertainty of the parameters is considered in GCSI based on the PSO-ELM. The results show that compared with the ELM, the PSO-ELM can establish the surrogate model with higher accuracy. The surrogate model based on PSO-ELM can be embedded in the optimization model to effectively solve GCSI problems. The predictive model based on PSO-ELM can predict the release histories of contamination sources corresponding to different random parameters.
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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
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影响因子：
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DOI：
10.1061/(asce)0733-9496(2004)130:6(506
发表时间：
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影响因子：
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影响因子：
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