基于U-D分解卡尔曼滤波地下水污染源溯源辨识

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Abstract This paper uses U-D factorization Kalman filtering and a nonlinear programming optimization model to identify the number, location, and release intensity of groundwater pollution sources. Based on a hypothetical example, a numerical simulation model of groundwater contamination was established, and the parameters having large influences on the model were selected as random variables in the model by using sensitivity analysis. Then, Kalman filtering based on U-D factorization was used to identify the number and location of pollution sources. On the basis of these processes, an optimization model for identifying the release intensity of contamination sources was established, and a Kriging interpolation method was used to establish an Surrogate model for the numerical simulation model of groundwater pollution transport, as an alternative of the simulation model, which was embedded in the optimization model as a constraint condition, and the genetic algorithm was applied to solve the optimization model. Finally, the source strength of groundwater pollution was identified. The results show that the Kalman filter method based on U-D factorization could ensure the stability of the filter and effectively identify the number and location of pollution sources; the nonlinear programming optimization model could identify the release intensity of pollution sources. In the process of solving the optimization model, a substitute model of the simulation model embedded the optimization model was built with the Kriging method, which could greatly reduce the calculation load and time under the condition of a certain accuracy.

Key words： groundwater contamination source identification Kalman filter with UD factorization Surrogate model genetic algorithm

Received: 10 June 2020

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X523

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	JIA Shun-qing
	LU Wen-xi
	LI Jiu-hui
	BAI Yu-kun

Cite this article:

JIA Shun-qing,LU Wen-xi,LI Jiu-hui等. Inversion identification of groundwater contamination source based on U-D factorization Kalman filter[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(2): 713-719.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I2/713

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