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

doi:10.1088/1674-1056/28/2/024213

中国物理B ›› 2019, Vol. 28 ›› Issue (2): 24213-024213.doi: 10.1088/1674-1056/28/2/024213

• ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS • 上一篇下一篇

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

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)

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

收稿日期:2018-07-13 修回日期:2018-10-14 出版日期:2019-02-05 发布日期:2019-02-05
通讯作者: Lai-An Qin E-mail:laqin@aiofm.ac.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 41405014).

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

Received:2018-07-13 Revised:2018-10-14 Online:2019-02-05 Published:2019-02-05
Contact: Lai-An Qin E-mail:laqin@aiofm.ac.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 41405014).

摘要/Abstract

摘要： 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.

关键词: visibility, neural network, lidar signals, extinction coefficient

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.

Key words: visibility, neural network, lidar signals, extinction coefficient

中图分类号: (Effects of air pollution)

42.68.Kh

42.68.Wt (Remote sensing; LIDAR and adaptive systems) 42.79.Ta (Optical computers, logic elements, interconnects, switches; neural networks)

孙国栋, 秦来安, 侯再红, 靖旭, 何枫, 谭逢富, 张巳龙, 张守川. Feasibility analysis for acquiring visibility based on lidar signal using genetic algorithm-optimized back propagation algorithm[J]. 中国物理B, 2019, 28(2): 24213-024213.

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[J]. Chin. Phys. B, 2019, 28(2): 24213-024213.

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

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

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