Groundwater Pollution Sources Identification Based on Hybrid Homotopy-Genetic Algorithm and Simulation Optimization

2021 ◽  
Author(s):  
Jiuhui Li ◽  
Wenxi Lu ◽  
Yue Fan
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Pollution ◽  
Simulation Optimization ◽  
Pollution Sources ◽  
Sources Identification

scholarly journals Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Water ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 193 ◽  
Author(s):  
Linxian Huang ◽  
Lichun Wang ◽  
Yongyong Zhang ◽  
Liting Xing ◽  
Qichen Hao ◽  
...  
Keyword(s):  
Groundwater Pollution ◽  
Optimization Model ◽  
Simulation Optimization ◽  
Pollution Sources

scholarly journals Genetic algorithm hyper-parameter optimization using Taguchi design for groundwater pollution source identification

2018 ◽  
Vol 19 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Xuemin Xia ◽  
Simin Jiang ◽  
Nianqing Zhou ◽  
Xianwen Li ◽  
Lichun Wang
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Pollution ◽  
Parameter Tuning ◽  
Pollution Source ◽  
Heterogeneous Porous Media ◽  
Optimization Approach ◽  
Human Beings ◽  
Transient Transport ◽  
Tuning Method ◽  
Pollutant Source

Abstract Groundwater pollution has been a major concern for human beings, since it is inherently related to people's health and fitness and the ecological environment. To improve the identification of groundwater pollution, many optimization approaches have been developed. Among them, the genetic algorithm (GA) is widely used with its performance depending on the hyper-parameters. In this study, a simulation–optimization approach, i.e., a transport simulation model with a genetic optimization algorithm, was utilized to determine the pollutant source fluxes. We proposed a robust method for tuning the hyper-parameters based on Taguchi experimental design to optimize the performance of the GA. The effectiveness of the method was tested on an irregular geometry and heterogeneous porous media considering steady-state flow and transient transport conditions. Compared with traditional GA with default hyper-parameters, our proposed hyper-parameter tuning method is able to provide appropriate parameters for running the GA, and can more efficiently identify groundwater pollution.

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