基于替代模型和流向算法的地下水污染源反演识别

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Abstract In this paper, the theory and method of simulation-optimization was applied to identify the relevant information of groundwater pollution sources, the hydraulic conductivities of the simulation model, and the pumping capacity of pumping wells simultaneously. First, a numerical simulation model of groundwater contamination was constructed based on a hypothetical example. Then, the BP neural network (BPNN) and kernel extreme learning machine (KELM) methods were applied to construct surrogate models of the simulation model, and the surrogate model with better fitting accuracy was selected and embedded in the subsequent optimization model to reduce the computational load and improve the approximation accuracy of the surrogate model to the simulation model. Finally, the inversion results were obtained by solving the optimized model with flow direction algorithm (FDA) and comparing them with those obtained by sparrow search algorithm (SSA) and particle swarm optimization (PSO) respectively. The results showed that compared with the KELM surrogate model, the BPNN surrogate model had higher fitting accuracy, with the coefficient of determination, the average relative error and the root mean square error of 0.9999, 0.1723% and 0.5625, respectively; compared with PSO and SSA, FDA had faster convergence speed and higher accuracy in solving optimization model. The average relative error of its identification results was less than 7%, which improved the accuracy and efficiency of groundwater pollution source inversion identification and provided a reliable basis for groundwater pollution remediation, risk assessment and liability determination.

Key words： simultaneous identification simulation-optimization method surrogate model flow direction algorithm BP neural network

Received: 06 April 2023

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X523

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	Articles by authors
	LUO Cheng-ming
	LU Wen-xi
	PAN Zi-dong
	WANG Zi-bo
	XU Ya-ning
	BAI Yu-kun

Cite this article:

LUO Cheng-ming,LU Wen-xi,PAN Zi-dong等. Inversion and identification of groundwater pollution sources based on surrogate model and flow direction algorithm[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(11): 5823-5832.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I11/5823

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