典型耦合优化算法在源项反演中的对比研究

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Abstract Rapid and accurate estimation of source items was the basis for environment emergency disposal on sudden air pollution accidents. In order to search for effective methods for inversing source parameters, we conducted a comparison study on the performances of three hybrid algorithms (e.g., GA-PSO, GA-NM, PSO-NM) for estimating source parameters (strength and location). Three inversion models were developed by combining GA-PSO, GA-NM, PSO-NM with Gaussian dispersion model, respectively. The study was carried out based upon SO₂ leakage tests selected from 1956 Prairie Grass emission experiment. The impacts of algorithm structure and atmospheric diffusion conditions on source term inversion were analyzed. Results showed that for source strength, the PSO-NM algorithm performed more accurate and robust, the mean error and mean standard deviation were 11.3% and 0.7g/s, respectively, which were much lower than those of GA-NM (i.e., 16.4%, 13.3g/s) and GA-PSO (i.e., 29.0%, 26.6g/s). As for source location, the performance of PSO-NM was more robust, with average standard deviation of 0.29m, which was also much lower than that of GA-NM (3.20m) and GA-PSO (3.03m). Under the unstable and neutral atmospheric diffusion conditions, the accuracy of PSO-NM algorithm for estimating position parameter was the best, with an error of 4.97m; However, GA-NM method had the minimum error (7.69m) under the stable condition. As for computational efficiency, PSO-NM and GA-PSO spent less time in source item inversion, which were more suitable for inversing source parameters for sudden air pollution.

Key words： hybrid algorithm 4-D source parameters Gauss point source plume diffusion model source parameter inversion sudden air pollution accidents

Received: 06 January 2019

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X511

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	Articles by authors
	SHEN Ze-ya
	LANG Jian-lei
	CHENG Shui-yuan
	MAO Shu-shuai
	CUI Ji-xian

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SHEN Ze-ya,LANG Jian-lei,CHENG Shui-yuan等. Comparative and study on the application of typical hybrid algorithms in source parameter inversions[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(8): 3207-3214.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I8/3207

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