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Homotopy-based hyper-heuristic searching approach for reciprocal feedback inversion of groundwater contamination source and aquifer parameters
Applied Soft Computing ( IF 8.7 ) Pub Date : 2021-02-18 , DOI: 10.1016/j.asoc.2021.107191
Zeyu Hou , Wangmei Lao , Yu Wang , Wenxi Lu

Groundwater contamination source identification is critical for taking effective measures to design remediation strategies, assess contamination risks, and confirm contamination responsibilities. To resolve the “equifinality” problem resulting from simultaneous inversion of contamination source characteristics and aquifer parameters at dense non-aqueous phase liquid-contaminated sites, two reciprocal optimization frames for separately identifying the contamination sources and aquifer parameters were designed and connected. The two sets of identification results were corrected stepwise by means of a feedback correction iteration process, thereby sufficiently improving the identification accuracy. The ensemble learning machine (ESLM) incorporating Kriging, radical basis function neural network, support vector regression, and wavelet kernel extreme learning machine with swarm intelligence (SI) algorithm was embedded into the reciprocal inversion iterations to replace the multiphase flow simulation model for significantly improving the computational efficiency. To improve the optimization efficiency, a hyper-heuristic homotopy algorithm was constructed for segmentally searching the global optimum in wider areas with low dependence on initial values. Results showed that the combined application of SI-based ensemble learning machine (SI-ESLM) and hyper-heuristic homotopy algorithm effectively accomplished the simultaneous identification of contamination sources and aquifer parameters with high efficiency, while maintaining high accuracy. The SI-ESLM sufficiently approximated the outputs of the multiphase flow simulation model with increased certainty (R2=0.9977), while the mean relative error was limited to 1.5388%. Compared to traditional heuristic algorithms, this application of reciprocal inversion iterations and the hyper-heuristic homotopy algorithm significantly reduced the mean relative error of identification results from 6.51% to 1.03%.



中文翻译:

基于同态的超启发式搜索方法,对地下水污染源和含水层参数进行反反馈

地下水污染源识别对于采取有效措施设计补救策略,评估污染风险并确认污染责任至关重要。为了解决在稠密非水相液体污染场所同时污染源特征和含水层参数同时反演而导致的“均等性”问题,设计并连接了两个相互独立的优化框架,分别用于识别污染源和含水层参数。通过反馈校正迭代过程逐步校正两组识别结果,从而充分提高了识别精度。集成了Kriging,基本功能神经网络,支持向量回归的集成学习机(ESLM),在逆反迭代中嵌入了具有群智能算法的小波核极限学习机,取代了多相流模拟模型,大大提高了计算效率。为了提高优化效率,构造了一种超启发式同伦算法,用于在较不依赖初始值的情况下在更广的区域中分段搜索全局最优。结果表明,基于SI的集成学习机(SI-ESLM)和超启发式同伦算法的组合应用有效地完成了污染源和含水层参数的同时识别,同时保持了较高的准确性。SI-ESLM具有更高的确定性,足以充分逼近多相流模拟模型的输出([R2个=0.9977),而平均相对误差限制为1.5388%。与传统的启发式算法相比,倒数反演迭代和超启发式同态算法的这种应用将识别结果的平均相对误差从6.51%显着降低到1.03%。

更新日期:2021-02-25
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