基于SSA-BP与SSA的地下水污染源反演识别

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Abstract The simulation-optimization method based on SSA-BP neural network alternative model and SSA were applied to study the inverse identification of groundwater pollution source location and release history. And the Cholesky decomposition method was applied to establish the continuous field of aquifer permeability coefficients in the groundwater flow model, which better describes the non-homogeneity of hydrogeological parameters compared with the common parameter partitioning method. The results showed that the SSA-BP neural network alternative model has a high approximation accuracy for the simulation model, and its average relative error is only 3.21%. The relative error of SSA in the inverse identification of source location is about 10%, and the relative error of SSA in the inverse identification of source intensity does not exceed 4%. Therefore, the proposed method is an effective groundwater pollution source identification method, which can provide reference for pollution responsibility identification and pollution remediation plan optimization.

Key words： pollution source inversion identification simulation-optimization method alternative model sparrow search algorithm SSA-BP neural network alternative model

Received: 21 April 2022

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X523

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	Articles by authors
	GE Yuan-bo
	LU Wen-xi
	BAI Yu-kun
	PAN Zi-dong

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GE Yuan-bo,LU Wen-xi,BAI Yu-kun等. Inversion and identification of groundwater pollution sources based on SSA-BP and SSA[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(11): 5179-5187.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I11/5179

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