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Chin. Phys. B, 2019, Vol. 28(2): 024213    DOI: 10.1088/1674-1056/28/2/024213
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS Prev   Next  

Feasibility analysis for acquiring visibility based on lidar signal using genetic algorithm-optimized back propagation algorithm

Guo-Dong Sun(孙国栋)1,2, Lai-An Qin(秦来安)2, Zai-Hong Hou(侯再红)2, Xu Jing(靖旭)2, Feng He(何枫)2, Feng-Fu Tan(谭逢富)2, Si-Long Zhang(张巳龙)2, Shou-Chuan Zhang(张守川)2
1 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China;
2 Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230031, China
Abstract  Visibility is an important atmospheric parameter that is gaining increasing global attention. This study introduces a back-propagation neural network method based on genetic algorithm optimization to obtain visibility directly using light detection and ranging (lidar) signals instead of acquiring extinction coefficient. We have validated the performance of the novel method by comparing it with the traditional inversion method, the back-propagation (BP) neural network method, and the Belfort, which is used as a standard value. The mean square error (MSE) and mean absolute percentage error (MAPE) values of the genetic algorithm-optimized back propagation (GABP) method are located in the range of 0.002 km2-0.005 km2 and 1%-3%, respectively. However, the MSE and MAPE values of the traditional inversion method and the BP method are significantly higher than those of the GABP method. Our results indicate that the proposed algorithm achieves better performance and can be used as a valuable new approach for visibility estimation.
Keywords:  visibility      neural network      lidar signals      extinction coefficient  
Received:  13 July 2018      Revised:  14 October 2018      Accepted manuscript online: 
PACS:  42.68.Kh (Effects of air pollution)  
  42.68.Wt (Remote sensing; LIDAR and adaptive systems)  
  42.79.Ta (Optical computers, logic elements, interconnects, switches; neural networks)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 41405014).
Corresponding Authors:  Lai-An Qin     E-mail:  laqin@aiofm.ac.cn

Cite this article: 

Guo-Dong Sun(孙国栋), Lai-An Qin(秦来安), Zai-Hong Hou(侯再红), Xu Jing(靖旭), Feng He(何枫), Feng-Fu Tan(谭逢富), Si-Long Zhang(张巳龙), Shou-Chuan Zhang(张守川) Feasibility analysis for acquiring visibility based on lidar signal using genetic algorithm-optimized back propagation algorithm 2019 Chin. Phys. B 28 024213

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