气候变化领域集成服务门户(CCPortal): Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms

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DOI	10.1016/j.advwatres.2020.103540
	Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms
	Zhao Y.; Qu R.; Xing Z.; Lu W.
发表日期	2020
ISSN	0309-1708
卷号	138
英文摘要	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. © 2020
关键词	Contamination Efficiency Genetic algorithms Groundwater Groundwater pollution Heuristic algorithms Knowledge acquisition Learning algorithms Machine learning Modular robots Particle swarm optimization (PSO)Quantum computers Contaminant concentrations Contaminant sources Extreme learning machine Groundwater contaminants Heuristic optimization algorithms Heuristic search algorithms Source characteristics Traditional genetic algorithms Computational efficiency accuracy assessment genetic algorithm groundwater pollution identification method machine learning optimization reliability analysis surrogate method
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131835
推荐引用方式 GB/T 7714	Zhao Y.,Qu R.,Xing Z.,et al. Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms[J]. Advances in Water Resources,2020,138.
APA	Zhao Y.,Qu R.,Xing Z.,&Lu W..(2020).Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms.,138.
MLA	Zhao Y.,et al."Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms".138(2020).