Atmospheric Quality Assessment Model Based on Immune Clonal Selection Algorithm

Abstract:

The immune clonal selection algorithm is used to optimize the parameters in the formula of S growth curve index in the paper, thus we can obtain an assessment model for atmospheric comprehensive pollution that is suitable to the cases of multi-pollutants. Moreover the proposed assessment model is applied in the field of atmosphere assessment. Experimental results show that the assessment method proposed for atmosphere quality has many advantages such as pellucid principle, physical explication and correct assessment results etc. It is a new effective approach for intelligence theory and technology applied in the field of environment. Therefore it has great potential in the field of assessment the atmospheric quality.

• Periodical: Advanced Materials Research (Volumes 255-260)
• Pages: 3013-3017
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.255-260.3013
• Online since: May 2011
• Authors: Li Min Wang, Xu Ming Han, Ming Li, Chi Jun Zhang, Yan Ting Zhang
• Keywords: Assessment Model, Immune Clonal Selection Algorithm, Parameter Optimization

References

[1] Liu Q, Wu X J. An improved immune Clonal selection Algorithm. JOURNAL OF SHAN DONG UNIVERSTYY (ENGINEERINGSCIENCE), 2009, 39(6): 8-10.

[2] Liu Ruochen. Immune Clonal Strategy Algorithm and Its Application. Xian: Xian University, (2005).

[3] Su S, Zhan D C, Xu X.F. Fuzzy scheduling with parallel machines batch process based on immune algorithm. Computer Integrated Manufacturing Systems, 2006, 12(8): 1252-1257.

[4] Rothney M P, Neumann M B. An artificial neural network model of energy expenditure using nonintegrated acceleration signals. Journal of Application Physiology, 2007, 103 (4): 1419-27.

DOI: 10.1152/japplphysiol.00429.2007

[5] Zeng J, Guo H F. Hu Y M. Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission. Journal of Environment Science, 2007, 19 (4): 427-31.

DOI: 10.1016/s1001-0742(07)60071-0

[6] Incerti G. Analysis of bioclimatic time series and their neural network-based classification to characterize drought risk patterns in South Italy. International Journal of Biometeorology, 2007, 51 (4): 253-263.

DOI: 10.1007/s00484-006-0071-6

[7] Elena G., Velzen V, Eimer J. Dissociating effecter and movement direction selection during the preparation of manual reaching movements: Evidence from lateralized ERP components. Clinical Neurophysiology, 2007, 118 (9): 2031-(2049).

DOI: 10.1016/j.clinph.2007.06.003

[8] De Castro L N, Timmis J. An artificial immune network for multimodal function optimization. Proc of IEEE congress on Evolutionary Computation. Hawaii, 2002, 1: 699-704.

DOI: 10.1109/cec.2002.1007011

[9] Xu R. The Research of Artificial Immune Algorithm Optimization and Applications. Shanghai: Shanghai University, (2009).

[10] Li Z Y, Wang Y, Liu G D. Optimum Estimation of Parameters on Assessment Model of Pollution Loss Rate of Atmospheric Environmental Quality Using Genetic Algorithm. Research of Environmental Sciences, 2001, 14(2): 7-10.

[11] Qian L W, Wu C Z, Hong W. Study on the Improvement of Index Formula of Pollution Harm for Atmosphere Quality Assessment. Journal of Fujian College of Forestry, 2003, 23(3): 249-252.

[12] Li, Z. Y. et al. A universal formula of pollution harm index suited to water quality assessment. Advances in Water Science, 2001, 12: 161-163.